Data-driven learning of physical systems has attracted significant attention, where many neural models have been developed. In particular, mesh-based graph neural networks (GNNs) have demonstrated considerable potential in modeling spatiotemporal dynamics across arbitrary geometric domains. However, the existing node-edge message-passing and aggregation mechanism in GNNs limits the representation learning capability. In this paper, we propose a dual-module framework, Cell-embedded and Feature-enhanced Graph Neural Network (CeFeGNN), for learning spatiotemporal dynamics. Specifically, we embed learnable cell attributions to the common node-edge message passing process, thereby better capturing the spatial dependency of regional features. Such a strategy essentially upgrades the local aggregation scheme from first order (e.g., from edge to node) to a higher order (e.g., from volume and edge to node), which takes advantage of volumetric information in message passing. Meanwhile, a novel feature-enhanced block is designed to further improve the model's performance and alleviate the over-smoothing problem. Extensive experiments on various PDE systems and a real-world dataset demonstrate that CeFeGNN achieves superior performance compared with other baselines.

Information access has evolved from search to ask to act. For more than two decades, information retrieval made networked information usable through crawling, indexing, ranking, user modeling, and recommendation. My early work on Web page understanding, vertical search, learning-to-rank, personalized search, and recommendation followed this user-centered view. This keynote briefly revisits that trajectory as the context for a large shift recently: from retrieving documents to constructing systems that reason over evidence, generate grounded answers, and execute information-seeking tasks.

Recently, generative recommendation has emerged as a promising paradigm, attracting significant research attention. The basic framework involves an item tokenizer, which represents each item as a sequence of codes serving as its identifier, and a generative recommender that predicts the next item by autoregressively generating the target item identifier. However, in existing methods, both the tokenizer and the recommender are typically domain-specific, limiting their ability for effective transfer or adaptation to new domains.

Information retrieval (IR) systems have traditionally been designed and trained for human users, with learning-to-rank methods relying heavily on large-scale human interaction logs such as clicks and dwell time. With the rapid emergence of large language model (LLM) powered search agents, however, retrieval is increasingly consumed by agents rather than human beings, and is embedded as a core component within multi-turn reasoning and action loops. In this setting, retrieval models trained under human-centric assumptions can be mismatched with the way agents issue intermediate queries and consume results. In this work, we argue that retrieval models for agentic search should be trained directly from agent interaction data. We study learning to retrieve from agent trajectories as a trajectory-supervised training setting, where supervision is derived from multi-step agent interactions. Through a systematic analysis of search agent trajectories, we identify key behavioral signals that reveal document utility, including browsing actions, unbrowsed rejections, and post-browse reasoning traces. Guided by these insights, we propose LRAT, a simple yet effective framework that mines high-quality retrieval supervision from agent trajectories and incorporates relevance intensity through weighted optimization. To instantiate this setting at scale, we deploy the Tongyi-DeepResearch-30B model on 10K InfoSeekQA queries with four retrievers, collecting 26,482 agent trajectories and constructing 91,713 training pairs. Extensive experiments on both in-domain and out-of-domain deep research benchmarks demonstrate that retrievers trained with LRAT consistently improve evidence recall, end-to-end task success, and execution efficiency across diverse agent architectures and scales. Our results highlight agent trajectories as a practical and scalable supervision source for retrieval in agentic search.

Click-through rate (CTR) prediction plays a pivotal role in online advertising and recommender systems. Despite notable progress in modeling user preferences from historical behaviors, two key challenges persist. First, exsiting discriminative paradigms focus on matching candidates to user history, often overfitting to historically dominant features and failing to adapt to rapid interest shifts. Second, a critical information chasm emerges from the point-wise ranking paradigm. By scoring each candidate in isolation, CTR models discard the rich contextual signal implied by the recalled set as a whole, leading to a misalignment where long-term preferences often override the user's immediate, evolving intent. To address these issues, we propose GenCI, a generative user intent framework that leverages semantic interest cohorts to model dynamic user preferences for CTR prediction. The framework first employs a generative model, trained with a next-item prediction (NTP) objective, to proactively produce candidate interest cohorts. These cohorts serve as explicit, candidate-agnostic representations of a user's immediate intent. A hierarchical candidate-aware network then injects this rich contextual signal into the ranking stage, refining them with cross-attention to align with both user history and the target item. The entire model is trained end-to-end, creating a more aligned and effective CTR prediction pipeline. Extensive experiments on three widely used datasets demonstrate the effectiveness of our approach.

Click-through rate (CTR) prediction holds a pivotal role in online advertising and recommendation systems, where even small improvements can significantly boost revenue. Existing research primarily focuses on designing dual-stream architectures to capture effective complex feature interactions from both explicit and implicit perspectives. However, these approaches are faced with two major challenges: (1) the high complexity of feature interaction learning, which increases computational demands and the overfitting risk, and (2) the imbalance between explicit and implicit modules, where one module’s output may dominate the final prediction. To address these issues, in this article, we propose dual-stream multi-layer perceptron (DS-MLP) , a novel feature interaction framework for the CTR prediction task. Specially, it leverages knowledge distillation to consolidate the capacity of learning explicit feature interaction into a main MLP network, while a parallel MLP simultaneously captures implicit feature interactions as a complement. To effectively optimize the DS-MLP architecture, we further design a specific learning approach with two alignment strategies for enhancing the compatibility of the two MLP components. Experiments demonstrate that DS-MLP , though merely a vanilla MLP structure (the final model), can achieve state-of-the-art performance across three widely used benchmarks, offering a scalable and efficient solution for large-scale recommendation systems. Our code is available at https://github.com/RUCAIBox/DS-MLP .

Personalized search has been proven to be an effective method to improve ranking quality by tailoring result lists according to the user's search history. Previous studies achieve personalization by learning a user interest profile from the search log, and decide the candidate document's ranking score by calculating its relevance with the learned profile vector. However, existing approaches overlook fine-grained interaction signals by treating the candidate document separately from the user's search history, relying solely on comparisons with a unified interest vector for re-ranking. Leveraging history-document interactions is not trivial due to the challenge of assessing the contributions of fine-grained matching signals within complex evolving interest patterns. In this paper, we address this challenge by helping the model understand these interactions within the evolving interest process through their integration into the interest profiling procedure. Specifically, we hierarchically incorporate these interaction signals as document-aware interests into behavior representations, employing explicit balancing and differentiation mechanisms, while jointly learning the interest pattern from both actual clues derived from original interests and potential insights provided by document-aware interests. Experimental results show that our model obtains substantial improvements over existing methods.

Search result diversification focuses on providing relevant and diverse documents covering different users’ intents. Intuitively, training an effective and stable search result diversification model needs a large amount of training data. Unfortunately, annotating such data that encompass real users’ search intents is expensive and time-consuming, and most existing models are trained with limited training data, which might lead to unsatisfactory ranking results. Given that Wikipedia contains massive amounts of rigorous editorial and well-structured data, in this article, we propose a pre-training framework leveraging the large-scale Wikipedia data to build weak-supervised signals. Specifically, we introduce four strategies to extract paired supervised signals reflecting the subtopic coverage information from Wikipedia. We also propose a subtopic-disentangled negative sampling strategy to sample hard negative samples and enhance the model’s ability to identify subtle subtopic differences. Four auxiliary tasks are devised to pre-train the Transformer model, which is further adopted as the representation generation model in the downstream diversified ranking. Experimental results demonstrate that our pre-trained model can significantly improve the performance of several existing models, which confirms the effectiveness and scalability of pre-training for search result diversification.

The diversified recommendation aims to satisfy a user’s different preferences and hence alleviates the information cocoon problem. Previous methods focus on increasing the sample probability of interacted items in the long-tail category. However, these methods are limited by the scope of the historical interactions of a single user and confront the challenge of inadequate diversity of past interactions and unpredictable potential diverse preferences. Drawing from social cognitive theory, observational learning ability allows humans to imitate others’ behaviors when their experience is insufficient. Inspired by it, in this article, we apply the idea of observational learning to the diversified recommendation and introduce a social Cog nitive Theory Enhanced D iversified R ecommendation ( Cog4DR ) model. Specifically, we design a three-step observational learning pipeline, including attention, purification, and retention, corresponding to the three essential stages of observational learning. The pipeline enables the current user to observe other users who have similar tastes but also engage with unique categories, therefore exploring potential diverse preferences and achieving dual improvements in accuracy and diversity. Experimental results indicate that Cog4DR outperforms all previous approaches, demonstrating the effectiveness of imitating other users’ behaviors for diversified recommendations.

Recent studies have shown that large language models (LLMs) can assess relevance and support information retrieval (IR) tasks such as document ranking and relevance judgment generation. However, the internal mechanisms by which off-the-shelf LLMs understand and operationalize relevance remain largely unexplored. In this article, we systematically investigate how different LLM modules contribute to relevance judgment through the lens of mechanistic interpretability. Using activation patching techniques, we analyze the roles of various model components and identify a multi-stage, progressive process in generating either pointwise or pairwise relevance judgment. Specifically, LLMs first extract query and document information in the early layers, then process relevance information according to instructions in the middle layers, and finally utilize specific attention heads in the later layers to generate relevance judgments in the required format. Our findings provide insights into the mechanisms underlying relevance assessment in LLMs, offering valuable implications for future research on leveraging LLMs for IR tasks.

This is an erratum for the article “Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach” published in ACM Trans. Inf. Syst. 43, 5, Article 114 (July 2025), 37 pages.

As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing complex contextual signals and semantic nuances, they still face challenges such as data scarcity, interpretability, and the generation of contextually plausible yet potentially inaccurate responses. This evolution requires a combination of traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity). Meanwhile, the emergence of large language models (LLMs) has revolutionized natural language processing due to their remarkable language understanding, generation, and reasoning abilities. Consequently, recent research has sought to leverage LLMs to improve IR systems. Given the rapid evolution of this research trajectory, it is necessary to consolidate existing methodologies and provide nuanced insights through a comprehensive overview. In this survey, we delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, readers, and search agents.

Recently, large language models (LLMs) have been introduced into recommender systems (RSs) as recommendation backbones or to enhance traditional recommendation models (TRMs). However, existing LLM-based RSs fail to fully leverage the complementary strengths of LLMs (e.g., world knowledge and reasoning capabilities) and TRMs (e.g., recommendation-specific knowledge and computational efficiency), resulting in shallow exploration of the item space. To address this limitation, we propose DeepRec, a novel LLM-based RS approach that facilitates autonomous multi-turn interactions between LLMs and TRMs for deep item space exploration. In each interaction turn, LLMs reason over user preferences and collaborate with TRMs to retrieve candidate items. After multi-turn interaction, LLMs rank the aggregated candidates to generate the final recommendations. We utilize reinforcement learning (RL) for optimization and introduce novel contributions in three key aspects: recommendation model based data rollout, recommendation-oriented hierarchical rewards, and a two-stage RL training strategy. For data rollout, we design a preference-aware TRM, with which LLMs interact to construct trajectory data. For reward design, we propose a hierarchical reward function that comprises both process-level and outcome-level rewards to optimize the interaction process and recommendation quality, respectively. For RL training, our two-stage RL strategy first guides LLMs to learn effective interactions with TRMs, followed by recommendation-oriented RL for performance enhancement. Experiments on public datasets show that DeepRec substantially outperforms both traditional and existing LLM-based baselines, establishing a new paradigm for deep exploration in recommender systems.

Conversational Retrieval-Augmented Generation (RAG) systems enhance user interactions by integrating large language models (LLMs) with external knowledge retrieval. However, multi-turn conversations present significant challenges, including implicit user intent and noisy context, which hinder accurate retrieval and response generation. Existing approaches often struggle with the unstructured conversational context and fail to model explicit relations among conversational turns. Moreover, they do not leverage historically relevant passages effectively. To overcome these limitations, we propose EvoRAG, a novel framework that maintains an evolving knowledge graph aligned with the unstructured conversational context. This graph explicitly captures relations among user queries, system responses, and relevant passages across conversational turns, serving as a structured representation of the context. EvoRAG includes three key components: (1) a dual-path retrieval module for context denoising, (2) a unified knowledge integration module for query rewriting and summarization, and (3) a graph-enhanced RAG module for accurate retrieval and response generation. Experiments on four public conversational RAG datasets show that EvoRAG significantly outperforms strong baselines, particularly in handling topic shifts and long dialogue contexts.

Search result diversification plays a crucial role in addressing query ambiguity and multi-faceted information needs by reducing redundancy across documents. While previous supervised approaches can achieve superior performance, they require costly, large-scale annotated data. In contrast, unsupervised methods are more flexible and training-free but rely on manually designed ranking functions, often leading to suboptimal performance. Inspired by how humans explore diverse information during real-world searching, we propose a diversified search agent DIVAgent to combine the advantages of supervised and unsupervised methods. DIVAgent introduces LLMs as the ''brain'' to reason over complex and diverse search results and delineate human cognitive processes into a workflow tailored for search result diversification. Our search agent first identifies potential user intents and then analyzes the alignment of each document to the intents via an intent-aware module. To guide the generation of diversified document rankings, we design an intent-guided ranker that explicitly links documents to their dominant intents while performing greedy document selection. Experimental results demonstrate that DIVAgent significantly outperforms existing unsupervised baselines and achieves competitive performance with supervised models, highlighting the promise of LLMs for diversified ranking in realistic search scenarios.

Recently, research found that the documents retrieved from the Retrieval-Augmented Generation (RAG) may contain conflicting knowledge with each other, leading Large Language Models (LLMs) to generate incorrect responses. To solve such a problem, existing approaches usually only keep the most frequently mentioned knowledge from these documents, since they assume that the most representative knowledge aligns best with the true answer. Although effective in certain scenarios, these approaches often underperform when the most frequent knowledge is not the correct one. From the voting perspective, these methods can be regarded as a Majority Voting (MV) process, which chooses the most frequent candidates among different candidate knowledge. However, we show that the underperformance of such methods stems from that MV is only effective with a small number of candidates and binary voting scores. In contrast, in the RAG scenario, the candidates (knowledge) are very diverse, and the voting scores (document relevance scores) are typically continuous. Simply adapting MV in RAG will result in poor performance of LLMs. In voting theory, on the other hand, the preference-based voting methods represented by the Borda Voting (BV) consider the whole preference order of voters over all candidates, enabling the selection of candidates that better represent the collective viewpoint. Inspired by such an insight, we propose BordaRAG, a model designed to better select the most appropriate documents from conflicting documents. Specifically, BordaRAG first computes the preference scores of the documents over the candidate answers. After that, a BV component is designed to select the winning documents according to the preference scores. Finally, the chosen documents are provided to LLMs, which will generate the final response. Experimental results on three open-domain QA datasets show that BordaRAG can outperform all baselines.

To satisfy users' diverse information needs, the aggregated search systems need to integrate heterogeneous results, with rich but different structural information, from a variety of verticals, such as news search, video search, and product search. A key challenge in aggregated search is to effectively and efficiently retrieve the most relevant results among a large number of heterogeneous information from different verticals. With the development of deep learning and pre-trained language models (PLMs), many researchers resort to Dense Retrieval (DR) models for a unified, efficient embedding-based retrieval and a better retrieval performance. However, existing dense retrieval models have limitations in: 1) capturing the structural information of search results ; and 2) generalizing across different vertical domains where the search results have different or even unseen structures. In this paper, we aim to tackle these limitations, and propose an effective and efficient dense retrieval model for aggregated search. Specifically, we utilize a deep prompt-tuning technique to make the pre-training model easily applied to downstream vertical search tasks. To capture the structural knowledge, we design a Graph Neural Network (GNN)-based structure prompt, to prompt how text segments are organized in the vanilla semi-structured data. We further incorporate a distributional prompt to model the theme of each domain, and enhance cross-domain generalization. Extensive experiments on the real-world data collected from the WeChat Search demonstrate that for aggregated search tasks, our models can achieve better performance over existing retrieval models, and have the superior ability to generalize to the various or even unseen vertical search tasks.

Unbiased learning to rank (ULTR), which aims to learn unbiased ranking models from biased user behavior logs, plays an important role in Web search. Previous research on ULTR has studied a variety of biases in users' clicks, such as position bias, presentation bias, and outlier bias. However, existing work often assumes that the behavior logs are collected from an ''average'' user, neglecting the differences between different users in their search and browsing behaviors. In this paper, we introduce personalized factors into the ULTR framework, which we term the user-aware ULTR problem. Through a formal causal analysis of this problem, we demonstrate that existing user-oblivious methods are biased when different users have different preferences over queries and personalized propensities of examining documents. To address such a personalized bias, we propose a novel user-aware inverse-propensity-score estimator for learning-to-rank objectives. Specifically, our approach models the distribution of user browsing behaviors for each query and aggregates user-weighted examination probabilities to determine propensities. We theoretically prove that the user-aware estimator is unbiased under some mild assumptions and shows lower variance compared to the straightforward way of calculating a user-dependent propensity for each impression. Finally, we empirically verify the effectiveness of our user-aware estimator by conducting extensive experiments on two semi-synthetic datasets and a real-world dataset.

In recent years, substantial research efforts have been devoted to enhancing sequential recommender systems by integrating abundant side information with ID-based collaborative information. This study specifically focuses on leveraging the textual metadata (e.g., titles and brands) associated with items. While existing methods have achieved notable success by combining text and ID representations, they often struggle to strike a balance between textual information embedded in text representations and collaborative information from sequential patterns of user behavior. In light of this, we propose CCFRec, a novel Code-based textual and Collaborative semantic Fusion method for sequential Recommendation. The key idea behind our approach is to bridge the gap between textual and collaborative information using semantic codes. Specifically, we generate fine-grained semantic codes from multi-view text embeddings through vector quantization techniques. Subsequently, we develop a code-guided semantic-fusion module based on the cross-attention mechanism to flexibly extract and integrate relevant information from text representations. In order to further enhance the fusion of textual and collaborative semantics, we introduce an optimization strategy that employs code masking with two specific objectives: masked code modeling and masked sequence alignment. The merit of these objectives lies in leveraging mask prediction tasks and augmented item representations to capture code correlations within individual items and enhance the sequence modeling of the recommendation backbone. Extensive experiments conducted on four public datasets demonstrate the superiority of CCFRec, showing significant improvements over various sequential recommendation models. Our code is available at https://github.com/RUCAIBox/CCFRec.

Recent advances in retrieval-augmented generation (RAG) furnish large language models (LLMs) with iterative retrievals of relevant information to handle complex multi-hop questions. These methods typically alternate between LLM reasoning and retrieval to accumulate external information into the LLM's context. However, the ever-growing context inherently imposes an increasing burden on the LLM to perceive connections among critical information pieces, with futile reasoning steps further exacerbating this overload issue. In this paper, we present KnowTrace, an elegant RAG framework to (1) mitigate the context overload and (2) bootstrap higher-quality multi-step reasoning. Instead of simply piling the retrieved contents, KnowTrace autonomously traces out desired knowledge triplets to organize a specific knowledge graph relevant to the input question. Such a structured workflow not only empowers the LLM with an intelligible context for inference, but also naturally inspires a reflective mechanism of knowledge backtracing to identify contributive LLM generations as process supervision data for self-bootstrapping. Extensive experiments show that KnowTrace consistently surpasses existing methods across three multi-hop question answering benchmarks, and the bootstrapped version further amplifies the gains.

Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep learning models often lack interpretability, fail to obey intrinsic physics, and struggle to cope with the various domains. While geometry-based methods, e.g., graph neural networks (GNNs), have been proposed to further tackle these challenges, they still need to find the implicit physical laws from large datasets and rely excessively on rich labeled data. In this paper, we herein introduce the conservation-informed GNN (CiGNN), an end-to-end explainable learning framework, to learn spatiotemporal dynamics based on limited training data. The network is designed to conform to the general conservation law via symmetry, where conservative and non-conservative information passes over a multiscale space enhanced by a latent temporal marching strategy. The efficacy of our model has been verified in various spatiotemporal systems based on synthetic and real-world datasets, showing superiority over baseline models. Results demonstrate that CiGNN exhibits remarkable accuracy and generalizability, and is readily applicable to learning for prediction of various spatiotemporal dynamics in a spatial domain with complex geometry.

Recommender systems are essential for information access, allowing users to present their content for recommendation. With the rise of large language models (LLMs), AI-generated content (AIGC), primarily in the form of text, has become a central part of the content ecosystem. As AIGC becomes increasingly prevalent, it is important to understand how it affects the performance and dynamics of recommender systems. To this end, we construct an environment that incorporates AIGC to explore its short-term impact. The results from popular sequential recommendation models reveal that AIGC are ranked higher in the recommender system, reflecting the phenomenon of source. To further explore the long-term impact of AIGC, we introduce a feedback loop with realistic simulators. The results show that the model's preference for AIGC increases as the user clicks on AIGC rises and the model trains on simulated click data. This leads to two issues: In the short term, bias toward AIGC encourages LLM-based content creation, increasing AIGC content, and causing unfair traffic distribution. From a long-term perspective, our experiments also show that when AIGC dominates the content ecosystem after a feedback loop, it can lead to a decline in recommendation performance. To address these issues, we propose a debiasing method based on L1-loss optimization to maintain long-term content ecosystem balance. In a real-world environment with AIGC generated by mainstream LLMs, our method ensures a balance between AIGC and human-generated content in the ecosystem. The code and dataset are available at https://github.com/Yuqi-Zhou/Rec_SourceBias.

Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations. A fundamental challenge in CRSs lies in effectively understanding user preferences from conversations. Previous research primarily focuses on the issue of insufficient contextual information in conversations. They address this by introducing external knowledge sources, such as knowledge graphs, large language models (LLMs), and conversational recommendation corpora. Based on this, they design specific alignment strategies (e.g., prompt learning and instruction tuning) to integrate such knowledge for user preference understanding and item recommendation. However, user preferences can be multifaceted and complex, posing significant challenges for accurate recommendations even with access to abundant external knowledge. While interaction with users can clarify their true preferences, frequent user involvement may lead to a degraded user experience.

In modern information retrieval (IR), going beyond accuracy is crucial for maintaining a healthy ecosystem, particularly in meeting fairness and diversity requirements. To address these needs, various datasets, algorithms, and evaluation methods have been developed. These algorithms are often tested with different metrics, datasets, and experimental settings, making comparisons inconsistent and challenging. Consequently, there is an urgent need for a comprehensive IR toolkit, enabling standardized assessments of fairness- and diversity-aware algorithms across IR tasks. To address these issues, we introduce an open-source standardized toolkit called FairDiverse. First, FairDiverse provides a comprehensive framework for incorporating fairness- and diversity-aware approaches, including pre-processing, in-processing, and post-processing methods, into different pipeline stages of IR. Second, FairDiverse enables the evaluation of 29 fairness, and diversity algorithms across 16 base models for two fundamental IR tasks-search and recommendation-facilitating the establishment of a comprehensive benchmark. Finally, FairDiverse is highly extensible, offering multiple APIs to enable IR researchers to quickly develop their own fairness- and diversity-aware IR models, and allows for fair comparisons with existing baselines. The project is open-sourced on GitHub:~ https://github.com/XuChen0427/FairDiverse.

Generative AI search driven by large language models (LLMs) is reshaping information retrieval by offering end-to-end answers to complex queries, reducing users' reliance on manually browsing and summarizing multiple web pages. However, while this paradigm enhances convenience, it disrupts the feedback-driven improvement loop that has historically powered the evolution of traditional Web search. Web search can continuously improve their ranking models by collecting large-scale, fine-grained user feedback (e.g., clicks, dwell time) at the document level. In contrast, generative AI search operates through a much longer search pipeline-spanning query decomposition, document retrieval, and answer generation-yet typically receives only coarse-grained feedback on the final answer. This introduces a feedback loop disconnect, where user feedback for the final output cannot be effectively mapped back to specific system components, making it difficult to improve each intermediate stage and sustain the feedback loop.

Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community for its ability to leverage massive logged user interaction data to train ranking models. While the CLTR models can be theoretically unbiased when the user behavior assumption is correct and the propensity estimation is accurate, their effectiveness is usually empirically evaluated via simulation-based experiments due to a lack of widely available, large-scale, real click logs. However, many previous simulation-based experiments are somewhat limited because they may have one or more of the following deficiencies: 1) using a weak production ranker to generate initial ranked lists, 2) relying on a simplified user simulation model to simulate user clicks, and 3) generating a fixed number of synthetic click logs. As a result, the robustness of CLTR models in complex and diverse situations is largely unknown and needs further investigation.

Maintaining the long-term sustainability of recommender systems (RS) is crucial.Traditional RS evaluation methods primarily focus on the user's immediate feedback (e.g., click), however, they often overlook the long-term effect involved by the content creators.In the real world, content creators can strategically create and upload new items to the platform by analyzing users' feedback and preference trends.Although previous studies have attempted to model creator behaviors, they often overlook that such behaviors are under conditions of information asymmetry.This asymmetry arises because creators mainly access the user feedback on the items they produce, while the platform has access to the full spectrum of feedback data.However, existing RS simulators often fail to consider such a condition, making the long-term RS evaluation inaccurate.To bridge this gap, we propose a Large Language Model (LLM)empowered creator simulation agent named CreAgent.By utilizing the belief mechanism from game theory and the fast-and-slow thinking framework, we can simulate the creator's behaviors well under information asymmetry.Furthermore, to enhance CreAgent's simulation ability, we utilize Proximal Policy Optimization to fine-tune CreAgent.Our credibility validation experiments demonstrate that our simulation environment effectively aligns with the behaviors of real-world platforms and creators, thereby enhancing the reliability of long-term evaluations in RS.Furthermore, leveraging this simulator, we can examine whether RS algorithms, such as fairnessand diversity-aware methods, contribute to improving long-term performance for different stakeholders.

Retrieval-augmented generation (RAG) has effectively mitigated the hallucination problem of large language models (LLMs). However, the difficulty of aligning the retriever with the LLMs' diverse knowledge preferences inevitably poses a challenge in developing a reliable RAG system. To address this issue, we propose DPA-RAG, a universal framework designed to align diverse knowledge preferences within RAG systems. Specifically, we initially introduce a preference knowledge construction pipeline and incorporate five novel query augmentation strategies to alleviate preference data scarcity. Based on preference data, DPA-RAG accomplishes both external and internal preference alignment: 1) It jointly integrates pairwise, pointwise, and contrastive preference alignment abilities into the reranker, achieving external preference alignment among RAG components. 2) It further introduces a pre-aligned stage before vanilla Supervised Fine-tuning (SFT), enabling LLMs to implicitly capture knowledge aligned with their reasoning preferences, achieving LLMs' internal alignment. Experimental results across four knowledge-intensive QA datasets demonstrate that DPA-RAG outperforms all baselines and seamlessly integrates both black-box and open-sourced LLM readers. Further qualitative analysis and discussions provide empirical guidance for achieving reliable RAG systems. Our code and example dataset are available at https://github.com/dongguanting/DPA-RAG.

Large language models (LLMs), with advanced linguistic capabilities, have been employed in reranking tasks through a sequence-to-sequence approach. In this paradigm, multiple passages are reranked in a listwise manner and a textual reranked permutation is generated. However, due to the limited context window of LLMs, this reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. This not only increases computational costs but also restricts the LLM from fully capturing all the comparison information for all candidates. To address these challenges, we propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking. To achieve it, we first propose the relevance-aware listwise reranking framework, which incorporates explicit list-view relevance scores to improve reranking efficiency and enable global comparison across the entire candidate set. Second, to ensure the comparability of the computed scores, we propose self-calibrated training that uses point-view relevance assessments generated internally by the LLM itself to calibrate the list-view relevance assessments. Extensive experiments and comprehensive analysis on the BEIR benchmark and TREC Deep Learning Tracks demonstrate the effectiveness and efficiency of our proposed method.

Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs. The Web is a major source of external knowledge used in RAG systems, and many commercial RAG systems have used Web search engines as their major retrieval systems. Typically, such RAG systems retrieve search results, download HTML sources of the results, and then extract plain texts from the HTML sources. Plain text documents or chunks are fed into the LLMs to augment the generation. However, much of the structural and semantic information inherent in HTML, such as headings and table structures, is lost during this plain-text-based RAG process. To alleviate this problem, we propose HtmlRAG, which uses HTML instead of plain text as the format of retrieved knowledge in RAG. We believe HTML is better than plain text in modeling knowledge in external documents, and most LLMs possess robust capacities to understand HTML. However, utilizing HTML presents new challenges. HTML contains additional content such as tags, JavaScript, and CSS specifications, which bring extra input tokens and noise to the RAG system. To address this issue, we propose HTML cleaning, compression, and a two-step block-tree-based pruning strategy, to shorten the HTML while minimizing the loss of information. Experiments on six QA datasets confirm the superiority of using HTML in RAG systems. Our code and datasets are available at https://github.com/plageon/HtmlRAG.

Recently, Large Language Models (LLMs) such as ChatGPT have showcased remarkable abilities in solving general tasks, demonstrating the potential for applications in recommender systems. To assess how effectively LLMs can be used in recommendation tasks, our study primarily focuses on employing LLMs as recommender systems through prompt engineering. We propose a general framework for leveraging LLMs in recommendation tasks, focusing on the capabilities of LLMs as recommenders. To conduct our analysis, we formalize the input of LLMs for recommendation into natural language prompts with two key aspects and explain how our framework can be generalized to various recommendation scenarios. As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs. As for prompt engineering, we further analyze the impact of four important components of prompts, i.e., task descriptions, user interest modeling, candidate items construction, and prompting strategies. In each section, we first define and categorize concepts in line with the existing literature. Then, we propose inspiring research questions followed by detailed experiments on two public datasets, in order to systematically analyze the impact of different factors on recommendation performance. Based on our empirical analysis, we finally summarize promising directions to shed lights on future research.

In search sessions, a series of interactions in the context has been proven to be advantageous in capturing users’ search intents. Existing studies show that designing pre-training tasks and data augmentation strategies for session search improves the robustness and generalizability of the model. However, such data augmentation strategies only focus on changing the original session structure to learn a better representation. Ignoring information from outside the session, users’ diverse and complex intents cannot be learned well by simply reordering and deleting historical behaviors, proving that such strategies are limited and inadequate. In order to solve the problem of insufficient modeling under complex user intents, we propose exploiting information outside the original session. More specifically, in this paper, we sample queries and documents from the global click-on and follow-up session graph, alter an original session with these samples, and construct a new session that shares a similar user intent with the original one. Specifically, we design four data augmentation strategies based on session graphs in view of both one-hop and multi-hop structures to sample intent-associated query/document nodes. Experiments conducted on three large-scale public datasets demonstrate that our model outperforms the existing ad-hoc and context-aware document ranking models.

Existing studies have proven that pre-trained ranking models outperform pre-trained language models when it comes to ranking tasks. To pre-train such models, researchers have utilized large-scale search logs and clicks as weak-supervised signals of query-document relevance. However, search logs are incomplete and sparse. Different users with the same intent tend to use various forms of queries. It is hard for recorded clicks to sufficiently cover diverse relevance patterns between queries and documents. Moreover, the diverse intentions of a large user base lead to long-tail distributions of search intents. Deriving sufficient relevance signals from sparse clicks of these long-tail intents poses another challenge. Therefore, there is significant potential for exploring richer relevance signals beyond direct clicks to pre-train high-quality ranking models. To tackle this problem, we develop two exploratory data augmentation strategies that consider the diversity of query forms from local and global perspectives, hence mining potential and diverse relevance signals from search logs. A generative augmentation strategy is also devised to create supplementary positive samples, to enhance the ranking ability for long-tail query intents. We leverage a multi-level pairwise ranking objective and a contrastive learning approach to enable our model to capture fine-grained relevance patterns and be robust for noisy training samples. Experimental results on a large-scale public dataset and a commercial dataset confirm that our model, namely PRADA, can yield better ranking effectiveness over existing pre-trained ranking models.

A key challenge in personalized product search is to capture user’s preferences. Recent work attempted to model sequences of user historical behaviors, i.e., product purchase histories, to build user profiles and to personalize results accordingly. Although these approaches have demonstrated promising retrieval performances, we notice that most of them focus solely on the intra-sequence interactions between items. However, as there is usually a small amount of historical behavior data, the user profiles learned by these approaches could be very sensitive to the noise included in it. To tackle this problem, we propose incorporating out-of-sequence external information to enhance user modeling. More specifically, we inject the external item–item relations (e.g., belonging to the same brand), and query–query relations (e.g., the semantic similarities between them), into the intra-sequence interaction to learn better user profiles. In addition, we devise two auxiliary decoders, with the historical item sequence reconstruction task and the global item similarity prediction task, to further improve the reliability of user modeling. Experimental results on two datasets from simulated and real user search logs respectively show that the proposed personalized product search method outperforms existing approaches.

Simulating high quality user behavior data has always been a fundamental yet challenging problem in human-centered applications such as recommendation systems, social networks, among many others. The major difficulty of user behavior simulation originates from the intricate mechanism of human cognitive and decision processes. Recently, substantial evidence has suggested that by learning huge amounts of web knowledge, large language models (LLMs) can achieve human-like intelligence and generalization capabilities. Inspired by such capabilities, in this article, we take an initial step to study the potential of using LLMs for user behavior simulation in the recommendation domain. To make LLMs act like humans, we design profile, memory and action modules to equip them, building LLM-based agents to simulate real users. To enable interactions between different agents and observe their behavior patterns, we design a sandbox environment, where each agent can interact with the recommendation system, and different agents can converse with their friends via one-to-one chatting or one-to-many social broadcasting. In the experiments, we first demonstrate the believability of the agent-generated behaviors based on both subjective and objective evaluations. Then, to show the potential applications of our method, we simulate and study two social phenomena including (1) information cocoons and (2) user conformity behaviors. We find that controlling the personalization degree of recommendation algorithms and improving the heterogeneity of user social relations can be two effective strategies for alleviating the problem of information cocoon, and the conformity behaviors can be highly influenced by the amount of user social relations. To advance this direction, we have released our project at https://github.com/RUC-GSAI/YuLan-Rec .

Multi-stakeholder recommender systems involve various roles, such as users and providers. Previous work pointed out that max-min fairness (MMF) is a better metric to support weak providers. However, when considering MMF, the features or parameters of these roles vary over time, and how to ensure long-term provider MMF has become a significant challenge. We observed that recommendation feedback loops (RFL) will influence the provider MMF greatly in the long term. RFL means that recommender systems can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback. When utilizing the feedback, the recommender model will regard the unexposed items as negative. In this way, the tail provider will not get the opportunity to be exposed, and its items will always be considered negative samples. Such phenomena will become more and more serious in RFL. To alleviate the problem, this article proposes an online ranking model named Long-Term Provider Max-min Fairness (LTP-MMF). Theoretical analysis shows that the long-term regret of LTP-MMF enjoys a sub-linear bound. Experimental results on three public recommendation benchmarks demonstrated that LTP-MMF can outperform the baselines in the long term.

Large language model (LLM)-based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions. The key component to support agent-environment interactions is the memory of the agents. While previous studies have proposed many promising memory mechanisms, they are scattered in different papers, and there lacks a systematical review to summarize and compare these works from a holistic perspective, failing to abstract common and effective designing patterns for inspiring future studies. To bridge this gap, in this article, we propose a comprehensive survey on the memory mechanism of LLM-based agents. In specific, we first discuss “what is” and “why do we need” the memory in LLM-based agents. Then, we systematically review previous studies on how to design and evaluate the memory module. In addition, we also present many agent applications, where the memory module plays an important role. At last, we analyze the limitations of existing work and show important future directions. To keep up with the latest advances in this field, we create a repository at https://github.com/nuster1128/LLM_Agent_Memory_Survey .

With the widespread deployment of recommender systems on various online platforms, researchers are striving to develop transferable recommendation algorithms that can effectively adapt to new task scenarios without requiring the re-training of new recommenders. However, there have been challenges in dealing with explicit ID modeling in this context. Recently, researchers have drawn inspiration from the achievements of pre-trained language models (PLMs), making it possible to acquire ID-agnostic representations by utilizing the corresponding texts of items. These representations have shown to be transferable across diverse domains. However, while these methods demonstrate generalization, they are less proficient in making personalized recommendations as they learn universal representation. In light of this issue, we present a review-enhanced universal sequence representation learning approach named RUNSRec . Our goal is to not only comprehend universal user behavioral patterns across different domains but also capture their inherent preferences to make recommendations. Our approach makes three technical advancements toward this objective. Firstly, we introduce a lightweight item encoding architecture based on parametric whitening and mixture-of-experts enhanced adapter. It learns discriminative item textual representations by encoding their corresponding identity text and review text, with a discriminative keyword extraction method to enhance the representation identifiability. Secondly, we propose a universal sequence representation learning method that enables the training of transferable recommenders across diverse domains, based on two novel contrastive learning tasks. Furthermore, we introduce a personalized adapter tuning mechanism that enables the universal recommender to capture user personal preferences in a parameter-efficient way. By incorporating universal behavioral patterns learned during the pre-training stage and personalized user tastes captured through adapter tuning, our approach achieves a better balance between generalization and personalization in transferable recommender systems. Extensive experiments conducted on five real-world datasets have demonstrated the effectiveness of our proposed approach.

In the past few decades, recommender systems have attracted much attention in both research and industry communities. Existing recommendation models mainly learn the underlying user preference from historical behavior data (typically in the forms of item IDs), and then estimate the user–item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we develop a different recommendation paradigm, considering recommendation as instruction following by LLMs. The key idea is that the needs of a user can be expressed in natural language descriptions (called instructions ), so that LLMs can understand and further execute the instruction for fulfilling the recommendation. For this purpose, we instruction tune the 3B Flan-T5-XL, to better adapt LLMs to recommender systems. We first design a general instruction format for describing the preference, intention, and task form of a user in natural language. Then we manually design 39 instruction templates and automatically generate large amounts of user-personalized instruction data with varying types of preferences and intentions. To demonstrate the effectiveness of our approach, we instantiate the instructions into several widely studied recommendation (or search) tasks, and conduct extensive experiments with real-world datasets. Experiment results show that our approach can outperform several competitive baselines, including the powerful GPT-3.5, on these evaluation tasks. Our approach sheds light on developing user-friendly recommender systems, in which users can freely communicate with the system and obtain accurate recommendations via natural language instructions.

Recently, integrating external tools with Large Language Models (LLMs) has gained significant attention as an effective strategy to mitigate the limitations inherent in their pre-training data. However, real-world systems often incorporate a wide array of tools, making it impractical to input all tools into LLMs due to length limitations and latency constraints. Therefore, to fully exploit the potential of tool-augmented LLMs, it is crucial to develop an effective tool retrieval system. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions, frequently leading to the retrieval of redundant, similar tools. Consequently, these methods fail to provide a complete set of diverse tools necessary for addressing the multifaceted problems encountered by LLMs. In this paper, we propose a novel modelagnostic CO llaborative L earning-based T ool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools. Specifically, we first fine-tune the PLM-based retrieval models to capture the semantic relationships between queries and tools in the semantic learning stage. Subsequently, we construct three bipartite graphs among queries, scenes, and tools and introduce a dual-view graph collaborative learning framework to capture the intricate collaborative relationships among tools during the collaborative learning stage. Extensive experiments on both the open benchmark and the newly introduced ToolLens dataset show that COLT achieves superior performance. Notably, the performance of BERT-mini (11M) with our proposed model framework outperforms BERT-large (340M), which has 30 times more parameters. Furthermore, we will release ToolLens publicly to facilitate future research on tool retrieval.

With the popularity of online music services, personalized music recommendation has garnered much research interest. Recommendation models are typically trained on datasets constructed from user feedback, which includes both the active feedback (e.g., clicking the Like or Skip buttons) and passive feedback (e.g., auto-play), with passive feedback comprising the majority. Due to the unavailability of user attention, the massive amount of passive feedback is unreliable, significantly compromising the quality of the training data. How to estimate the user's attention on the target music has become a critical problem in music recommendation. Heuristic methods such as exponential decay and negative sampling have been proposed. However, they either neglect the sequential dependencies between feedback actions or utilize only a small fraction of passive samples, leading to inaccurate and biased attention estimation. In this paper, we naturally propose modeling user attention prediction as a positive-unlabeled (PU) learning problem, where active feedback is treated as positive samples and passive feedback is treated as unlabeled samples, as we can only ensure that the user's attention is focused when she provides active feedback. Then we propose an extended PU-learning model with sequential dependencies, called UAE, which contains an unbiased user attention estimator and an unbiased propensity estimator. Subsequently, a joint learning algorithm is developed in which the attention and propensity estimators are optimized in alternating fashion. Theoretical analysis shows the unbiasedness and variance of the attention estimator and the propensity estimator. Extensive experiments on two large-scale datasets demonstrate the proposed UAE's effectiveness and generality in enhancing downstream music recommendation. One week online A/B testing on Huawei Music App manifests that UAE can significantly increase the users' play count and time over 2%, further demonstrating the effectiveness of UAE in real-world music recommendation products.

Recently, geometric-inspired embedding methods draw research interests for their superior ability in representing transitive and asymmetric relations. A typical example, box embeddings, in which objects are parameterized as axis-aligned hyper-rectangles (i.e. boxes), can effectively model the partial orders and similarities between objects with the inclusion and overlapping relations of the boxes. However, the hard edges of the boxes present difficulties for gradient-based optimization. In this paper, we first identify two problems that may hinder the optimization of box embeddings, namely the zero-gradient problem and the vanishing gradient problem in high dimensional settings. Then, we propose a simple yet effective framework (Box++) to tackle these problems. For the first problem, Box++ combines the overlapping volumes and distance measures in optimizing box embeddings. The distance measures can naturally measure the “degree of disjointedness” for disjoint boxes and provide reasonable gradients for optimization. For the second problem, we theoretically prove that under certain conditions, the gradient would vanish exponentially, and therefore, make the optimization converges to suboptimal solutions. We further design a gradient normalization strategy for the Box++ models to alleviate this problem by rescaling the vanishing gradient. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed framework.

Recently, large language models (LLMs) have shown great potential in recommender systems, either improving existing recommendation models or serving as the backbone. However, there exists a large semantic gap between LLMs and recommender systems, since items to be recommended are often indexed by discrete identifiers (item ID) out of the LLM's vocabulary. In essence, LLMs capture language semantics while recommender systems imply collaborative semantics, making it difficult to sufficiently leverage the model capacity of LLMs for recommendation. To address this challenge, in this paper, we propose a new LLM-based recommendation model called LC-Rec, which can better integrate language and collaborative semantics for recommender systems. Our approach can directly generate items from the entire item set for recommendation, without relying on candidate items. Specifically, we make two major contributions in our approach. For item indexing, we design a learning-based vector quantization method with uniform semantic mapping, which can assign meaningful and non-conflicting IDs (called item indices) for items. For alignment tuning, we propose a series of specially designed tuning tasks to enhance the integration of collaborative semantics in LLMs. Our fine-tuning tasks enforce LLMs to deeply integrate language and collaborative semantics (characterized by the learned item indices), so as to achieve an effective adaptation to recommender systems. Extensive experiments demonstrate the effectiveness of our method, showing that our approach can outperform a number of competitive baselines including traditional recommenders and existing LLM-based recommenders. Our code is available at https://github.com/RUCAIBox/LC-Rec/.

Personalized PageRank (PPR) is an extensively studied and applied node proximity measure in graphs. For a pair of nodes $s$ and $t$ on a graph $G=(V,E)$, the PPR value $π(s,t)$ is defined as the probability that an $α$-discounted random walk from $s$ terminates at $t$, where the walk terminates with probability $α$ at each step. We study the classic Single-Source PPR query, which asks for PPR approximations from a given source node $s$ to all nodes in the graph. Specifically, we aim to provide approximations with absolute error guarantees, ensuring that the resultant PPR estimates $\hatπ(s,t)$ satisfy $\max_{t\in V}\big|\hatπ(s,t)-π(s,t)\big|\le\varepsilon$ for a given error bound $\varepsilon$. We propose an algorithm that achieves this with high probability, with an expected running time of - $\widetilde{O}\big(\sqrt{m}/\varepsilon\big)$ for directed graphs, where $m=|E|$; - $\widetilde{O}\big(\sqrt{d_{\mathrm{max}}}/\varepsilon\big)$ for undirected graphs, where $d_{\mathrm{max}}$ is the maximum node degree in the graph; - $\widetilde{O}\left(n^{γ-1/2}/\varepsilon\right)$ for power-law graphs, where $n=|V|$ and $γ\in\left(\frac{1}{2},1\right)$ is the extent of the power law. These sublinear bounds improve upon existing results. We also study the case when degree-normalized absolute error guarantees are desired, requiring $\max_{t\in V}\big|\hatπ(s,t)/d(t)-π(s,t)/d(t)\big|\le\varepsilon_d$ for a given error bound $\varepsilon_d$, where the graph is undirected and $d(t)$ is the degree of node $t$. We give an algorithm that provides this error guarantee with high probability, achieving an expected complexity of $\widetilde{O}\left(\sqrt{\sum_{t\in V}π(s,t)/d(t)}\big/\varepsilon_d\right)$. This improves over the previously known $O(1/\varepsilon_d)$ complexity.

Feature interaction learning (FIL) focuses on capturing the complex relationships among multiple features for building predictive models, which is widely used in real-world tasks. Despite the research progress, existing FIL methods suffer from two major limitations. Firstly, they mainly model the feature interactions within a bounded order (e.g., small integer order) due to the exponential growth of the interaction terms. Secondly, the interaction order of each feature is often independently learned, which lacks the flexibility to capture the feature dependencies in varying contexts.

In video recommendation, an ongoing effort is to satisfy users' personalized information needs by leveraging their logged watch time. However, watch time prediction suffers from duration bias, hindering its ability to reflect users' interests accurately. Existing label-correction approaches attempt to uncover user interests through grouping and normalizing observed watch time according to video duration. Although effective to some extent, we found that these approaches regard completely played records (i.e., a user watches the entire video) as equally high interest, which deviates from what we observed on real datasets: users have varied explicit feedback proportion when completely playing videos. In this paper, we introduce the counterfactual watch time (CWT), the potential watch time a user would spend on the video if its duration is sufficiently long. Analysis shows that the duration bias is caused by the truncation of CWT due to the video duration limitation, which usually occurs on those completely played records. Besides, a Counterfactual Watch Model (CWM) is proposed, revealing that CWT equals the time users get the maximum benefit from video recommender systems. Moreover, a cost-based transform function is defined to transform the CWT into the estimation of user interest, and the model can be learned by optimizing a counterfactual likelihood function defined over observed user watch times. Extensive experiments on three real video recommendation datasets and online A/B testing demonstrated that CWM effectively enhanced video recommendation accuracy and counteracted the duration bias.

At present, most recommender systems involve two stakeholders, providers and customers. Apart from maximizing the recommendation accuracy, the fairness issue for both sides should also be considered. Most of previous studies try to improve two-sided fairness with post-processing algorithms or fairness-aware loss constraints, which are highly dependent on the heuristic adjustments without respect to the optimization goal of accuracy. In contrast, we propose a novel training framework, adaptive weighting towards two-sided fairness-aware recommendation (named Ada2Fair), which lies in the extension of the accuracy-focused objective to a controllable preference learning loss over the interaction data. Specifically, we adjust the optimization scale of an interaction sample with an adaptive weight generator, and estimate the two-sided fairness-aware weights within model training. During the training process, the recommender is trained with two-sided fairness-aware weights to boost the utility of niche providers and inactive customers in a unified way. Extensive experiments on three public datasets verify the effectiveness of Ada2Fair, which can achieve Pareto efficiency in two-sided fairness-aware recommendation.

Scaling of neural networks has recently shown great potential to improve the model capacity in various fields. Specifically, model performance has a power-law relationship with model size or data size, which provides important guidance for the development of large-scale models. However, there is still limited understanding on the scaling effect of user behavior models in recommender systems, where the unique data characteristics (e.g., data scarcity and sparsity) pose new challenges in recommendation tasks.

The vital goal of information retrieval today extends beyond merely connecting users with relevant information they search for. It also aims to enrich the diversity, personalization, and interactivity of that connection, ensuring the information retrieval process is as seamless, beneficial, and supportive as possible in the global digital era. Current information retrieval systems often encounter challenges like a constrained understanding of queries, static and inflexible responses, limited personalization, and restricted interactivity. With the advent of large language models (LLMs), there's a transformative paradigm shift as we integrate LLM-powered agents into these systems. These agents bring forth crucial human capabilities like memory and planning to make them behave like humans in completing various tasks, effectively enhancing user engagement and offering tailored interactions. In this tutorial, we delve into the cutting-edge techniques of LLM-powered agents across various information retrieval fields, such as search engines, social networks, recommender systems, and conversational assistants. We will also explore the prevailing challenges in seamlessly incorporating these agents and hint at prospective research avenues that can revolutionize the way of information retrieval.

To capture user preference, transformer models have been widely applied to model sequential user behavior data. The core of transformer architecture lies in the self-attention mechanism, which computes the pairwise attention scores in a sequence. Due to the permutation-equivariant nature, positional encoding is used to enhance the attention between token representations. In this setting, the pairwise attention scores can be derived by both semantic difference and positional difference. However, prior studies often model the two kinds of difference measurements in different ways, which potentially limits the expressive capacity of sequence modeling.

Search result diversification aims to provide a diversified document ranking list so as to cover as many intents as possible and satisfy the various information needs of different users. Existing approaches usually represented documents by pretrained embeddings (such as doc2vec and Glove). These document representations cannot adequately represent the document's content and are hard to capture the intrinsic user's intent coverage of the given query. Moreover, the limited number of labeled data for search result diversification exacerbates the difficulty of obtaining more efficient document representations. To alleviate these problems and learn more effective document representations, we propose a Contrastive Learning framework for search result DIVersification (CL4DIV). Specifically, we design three contrastive learning tasks from the perspective of subtopics, documents, and candidate document sequences, which correspond to three essential elements in search result diversification. These training tasks are employed to pretrain the document encoder and the document sequence encoder, which are used in the diversified ranking model. Experimental results show that øurs significantly outperforms all existing diversification models. Further analysis demonstrates that our method has wide applicability and can also be used to improve several existing methods.

Modeling contextual information in a search session has drawn more and more attention when understanding complex user intents. Recent methods are all data-driven, i.e., they train different models on large-scale search log data to identify the relevance between search contexts and candidate documents. The common training paradigm is to pair the search context with different candidate documents and train the model to rank the clicked documents higher than the unclicked ones. However, this paradigm neglects the symmetric nature of the relevance between the session context and document, i.e., the clicked documents can also be paired with different search contexts when training. In this work, we propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context, i.e., the current query, and train our model to rank the generated sequence along with the original sequence. This approach enables models to learn that the relevance of a document may vary as the session context changes, leading to a better understanding of users’ search patterns. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty. Through experimentation on two extensive public search logs, we have successfully demonstrated the effectiveness of our model.

Personalized search and search result diversification are two possible solutions to cope with the query ambiguity problem in search engines. In most existing studies, they have been investigated separately, but intuitively, they address the problem from two complementary perspectives and should be combined. Some recent work tried to combine them by restricting result diversification to the subtopics corresponding to the user's personal profile. However, diversification can be required even when the subtopics are outside the user's profile. In this paper, we propose a more general approach to integrate them based on users' implicit feedback in query logs. The proposed approach PER+DIV aggregates a document's novelty score and personal relevance score dynamically according to how much the query falls into the user's interests. To train the model based on user clicks in the logs, we consider user click as a result of both personal relevance and result diversity and a new method is proposed to isolate and model these two factors. To evaluate the model, we design several diversified and personalized metrics in addition to the traditional click-based metrics. Experimental results on a large-scale query log dataset show that the proposed integrated method significantly outperforms the existing personalization and diversification approaches.

Providing human-understandable explanations for the matching predictions is still challenging for current legal case matching methods. One difficulty is that legal cases are semi-structured text documents with complicated case-case and case-law article correlations. To tackle the issue, we propose a novel graph optimal transport (GOT)-based legal case matching model that is able to provide not only the matching predictions but also plausible and faithful explanations for the prediction. The model, called GEIOT-Match, first constructs a heterogeneous graph to explicitly represent the semi-structured nature of legal cases and their associations with the law articles. Therefore, matching two legal cases amounts to identifying the rationales from the paired legal case sub-graphs in the heterogeneous graph and then aligning between them. An inverse optimal transport (IOT) model on graphs is learned to extract rationales from paired legal cases. The extracted rationales and the heterogeneous graph demonstrate the key legal characteristics of legal cases, which can be further used to conduct matching and generate explanations for the matching. Experimental results showed that GEIOT-Match outperformed state-of-the-art baselines in terms of matching prediction, rationale extraction, and natural language explanation generation.

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes$\boldsymbol{s}$and$\boldsymbol{t}$, the PPR value$\boldsymbol{\pi_{s}(t)}$equals the probability that an$\boldsymbol{\alpha }$-discounted random walk from$\boldsymbol{s}$terminates at$\boldsymbol{t}$and reflects the importance between$\boldsymbol{s}$and$\boldsymbol{t}$in a bidirectional way. As a generalization of Google's celebrated PageRank centrality, PPR has been extensively studied and has found multifaceted applications in many fields, such as network analysis, graph mining, and graph machine learning. Despite numerous studies devoted to PPR over the decades, efficient computation of PPR remains a challenging problem, and there is a dearth of systematic summaries and comparisons of existing algorithms. In this paper, we recap several frequently used techniques for PPR computation and conduct a comprehensive survey of various recent PPR algorithms from an algorithmic perspective. We classify these approaches based on the types of queries they address and review their methodologies and contributions. We also discuss some representative algorithms for computing PPR on dynamic graphs and in parallel or distributed environments.

Group-fair recommendation aims at ensuring the equality of recommendation results across user groups categorized by sensitive attributes (e.g., gender, occupation, etc.). Existing group-fair recommendation models traditionally employ original user embeddings for both training and testing, primarily focusing on statistical learning while imposing group fairness constraints under the I.I.D. assumption. However, these models encounter limitations when addressing out-of-distribution (OOD) sensitive attributes. The fundamental issue of unfairness within user embeddings arises from a causal perspective, where each embedding vector comprises an exogenous component devoid of correlations with sensitive attributes and an endogenous component strongly correlated with these attributes. Overlooking the distinction between these two components during model training renders models sensitive to shifts in the distribution of sensitive attributes. This paper introduces the concept of Counterfactual Group Fairness (CGF) along with a corresponding metric to evaluate group fairness in scenarios involving OOD sensitive attributes in recommender systems. Building on this foundation, we propose a model-agnostic causal embedding learning framework named MACE. MACE effectively disentangles user embedding vectors into their exogenous and endogenous parts, thus ensuring group fairness, even in the presence of OOD sensitive attributes in embeddings. Specifically, MACE identifies the exogenous part of each user's embedding using mutual information minimization, treating it as instrumental variables. Subsequently, under the constraint of CGF, MACE reconstructs the endogenous and exogenous parts using the instrumental variable regression, combines the obtained parts into novel user embeddings using deep neural networks, and uses the combined embeddings for fair recommendation. Experimental results demonstrated that MACE can outperform the state-of-the-art baselines in terms of the metric of CGF while maintaining a comparable recommendation accuracy.

Document retrieval has been extensively studied within theindex-retrieveframework for decades, which has withstood the test of time. However, this approach inherently segregates the indexing and retrieval processes, preventing a cohesive, end-to-end optimization. To bridge this divide, we introduce WebUltron, a revolutionary model-centric indexer for document retrieval. This system embeds the entirety of document knowledge within the model, striving for seamless end-to-end retrieval. Two primary challenges with this indexer are the representation of document identifiers (docids) and the model's training. Current methods grapple with docids that lack semantic depth and the constraints of limited supervised data, making scaling up to larger datasets challenging. Addressing this, we've engineered two novel docid types imbued with richer semantics that also streamline model inference. Further enhancing WebUltron's capabilities, we've developed a three-stage training regimen, leveraging deeper corpus insights and fortifying query-docid relationships. Experiments on two public datasets demonstrate the superiority of WebUltron over advanced baselines for document retrieval.

Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use “static” document representations as the inputs, which do not change with the current search. However, a document is usually long and contains multiple pieces of information, a static fix-length document vector is usually insufficient to represent the important information related to the original query or the current query, and makes the profile noisy and ambiguous. To tackle this problem, we propose building dynamic and intent-oriented document representations which highlight important parts of a document rather than simply encode the entire text. Specifically, we divide each document into multiple passages, and then separately use the original query and the current query to interact with the passages. Thereafter we generate two “dynamic” document representations containing the key information around the historical and the current user intent, respectively. We then profile interest by capturing the interactions between these document representations, the historical queries, and the current query. Experimental results on a real-world search log dataset demonstrate that our model significantly outperforms state-of-the-art personalization methods.

Search result diversification plays a crucial role in improving users’ search experience by providing users with documents covering more subtopics. Previous studies have made great progress in leveraging inter-document interactions to measure the similarity among documents. However, different parts of the document may embody different subtopics and existing models ignore the subtle similarities and differences of content within each document. In this article, we propose a hierarchical attention framework to combine intra-document interactions with inter-document interactions in a complementary manner in order to conduct multi-grained document modeling. Specifically, we separate the document into passages to model the document content from multi-grained perspectives. Then, we design stacked interaction blocks to conduct inter-document and intra-document interactions. Moreover, to measure the subtopic coverage of each document more accurately, we propose a passage-aware document-subtopic interaction to perform fine-grained document-subtopic interaction. Experimental results demonstrate that our model achieves state-of-the-art performance compared with existing methods.

With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector, the recently proposed late-interaction multi-vector models (i.e., ColBERT and COIL) achieve state-of-the-art retrieval effectiveness by using all token embeddings to represent documents and queries and modeling their relevance with a sum-of-max operation. However, these fine-grained representations may cause unacceptable storage overhead for practical search systems. In this study, we systematically analyze the matching mechanism of these late-interaction models and show that the sum-of-max operation heavily relies on the co-occurrence signals and some important words in the document. Based on these findings, we then propose several simple document pruning methods to reduce the storage overhead and compare the effectiveness of different pruning methods on different late-interaction models. We also leverage query pruning methods to further reduce the retrieval latency. We conduct extensive experiments on both in-domain and out-domain datasets and show that some of the used pruning methods can significantly improve the efficiency of these late-interaction models without substantially hurting their retrieval effectiveness.

Point-of-interest (POI) search is important for location-based services, such as navigation and online ride-hailing service. The goal of POI search is to find the most relevant destinations from a large-scale POI database given a text query. To improve the effectiveness and efficiency of POI search, most existing approaches are based on a multi-stage pipeline that consists of an efficiency-oriented retrieval stage and one or more effectiveness-oriented re-rank stages. In this article, we focus on the first efficiency-oriented retrieval stage of the POI search. We first identify the limitations of existing first-stage POI retrieval models in capturing the semantic-geography relationship and modeling the fine-grained geographical context information. Then, we propose a Geo-Enhanced Dense Retrieval framework for POI search to alleviate the above problems. Specifically, the proposed framework leverages the capacity of pre-trained language models (e.g., BERT) and designs a pre-training approach to better model the semantic match between the query prefix and POIs. With the POI collection, we first perform a token-level pre-training task based on a geographical-sensitive masked language prediction and design two retrieval-oriented pre-training tasks that link the address of each POI to its name and geo-location. With the user behavior logs collected from an online POI search system, we design two additional pre-training tasks based on users’ query reformulation behavior and the transitions between POIs. We also utilize a late-interaction network structure to model the fine-grained interactions between the text and geographical context information within an acceptable query latency. Extensive experiments on the real-world datasets collected from the Didichuxing application demonstrate that the proposed framework can achieve superior retrieval performance over existing first-stage POI retrieval methods.

Research on search result diversification strives to enhance the variety of subtopics within the list of search results. Existing studies usually treat a document as a whole and represent it with one fixed-length vector. However, considering that a long document could cover different aspects of a query, using a single vector to represent the document is usually insufficient. To tackle this problem, we propose to exploit multiple passages to better represent documents in search result diversification. Different passages of each document may reflect different subtopics of the query and comparison among the passages can improve result diversity. Specifically, we segment the entire document into multiple passages and train a classifier to filter out the irrelevant ones. Then the document diversity is measured based on several passages that can offer the information needs of the query. Thereafter, we devise a passage-aware search result diversification framework that takes into account the topic information contained in the selected document sequence and candidate documents. The candidate documents’ novelty is evaluated based on their passages while considering the dynamically selected document sequence. We conducted experiments on a commonly utilized dataset, and the results indicate that our proposed method performs better than the most leading methods.

Ambiguity in queries is a common problem in information retrieval. There are currently two solutions: search result personalization and diversification. The former aims to tailor results for different users based on their preferences, but the limitations are redundant results and incomplete capture of user intents. The goal of the latter is to return results that cover as many aspects related to the query as possible. It improves diversity yet loses personality and cannot return the exact results the user wants. Intuitively, such two solutions can complement each other and bring more satisfactory reranking results. In this article, we propose a novel framework, namely, PnD , to integrate personalization and diversification reasonably. We employ the degree of refinding to determine the weight of personalization dynamically. Moreover, to improve the diversity and relevance of reranked results simultaneously, we design a reset RNN structure (RRNN) with the “reset gate” to measure the influence of the newly selected document on novelty. Besides, we devise a “subtopic learning layer” to learn the virtual subtopics, which can yield fine-grained representations of queries, documents, and user profiles. Experimental results illustrate that our model can significantly outperform existing search result personalization and diversification methods.

Matching two natural language sentences is a fundamental problem in both natural language processing and information retrieval. Preliminary studies have shown that the syntactic structures help improve the matching accuracy, and different syntactic structures in natural language are complementary to sentence semantic understanding. Ideally, a matching model would leverage all syntactic information. Existing models, however, are only able to combine limited (usually one) types of syntactic information due to the complex and heterogeneous nature of the syntactic information. To deal with the problem, we propose a novel matching model, which formulates sentence matching as a representation learning task on a syntactic-informed heterogeneous graph. The model, referred to as SIGN (Syntactic-Informed Graph Network), first constructs a heterogeneous matching graph based on the multiple syntactic structures of two input sentences. Then the graph attention network algorithm is applied to the matching graph to learn the high-level representations of the nodes. With the help of the graph learning framework, the multiple syntactic structures, as well as the word semantics, can be represented and interacted in the matching graph and therefore collectively enhance the matching accuracy. We conducted comprehensive experiments on three public datasets. The results demonstrate that SIGN outperforms the state of the art and also can discriminate the sentences in an interpretable way.

Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user’s queries in natural language. From heuristic-based retrieval methods to learning-based ranking functions, the underlying retrieval models have been continually evolved with the ever-lasting technical innovation. To design effective retrieval models, a key point lies in how to learn text representations and model the relevance matching. The recent success of pretrained language models (PLM) sheds light on developing more capable text-retrieval approaches by leveraging the excellent modeling capacity of PLMs. With powerful PLMs, we can effectively learn the semantic representations of queries and texts in the latent representation space, and further construct the semantic matching function between the dense vectors for relevance modeling. Such a retrieval approach is called dense retrieval , since it employs dense vectors to represent the texts. Considering the rapid progress on dense retrieval, this survey systematically reviews the recent progress on PLM-based dense retrieval. Different from previous surveys on dense retrieval, we take a new perspective to organize the related studies by four major aspects, including architecture, training, indexing and integration, and thoroughly summarize the mainstream techniques for each aspect. We extensively collect the recent advances on this topic, and include 300+ reference papers. To support our survey, we create a website for providing useful resources, and release a code repository for dense retrieval. This survey aims to provide a comprehensive, practical reference focused on the major progress for dense text retrieval.

In recent years, various dense retrieval methods have been developed to improve the performance of search engines with a vectorized index. However, these approaches require a large pre-computed index and have a limited capacity to memorize all semantics in a document within a single vector. To address these issues, researchers have explored end-to-end generative retrieval models that use a seq-to-seq generative model to directly return identifiers of relevant documents. Although these models have been effective, they are often trained with the MLE method. It only encourages the model to assign a high probability to the relevant document identifier, ignoring the relevance comparisons of other documents. This may lead to performance degradation in ranking tasks, where the core is to compare the relevance between documents. To address this issue, we propose a ranking-oriented generative retrieval model that incorporates relevance signals to better estimate the relative relevance of different documents in ranking tasks. Based upon the analysis of the optimization objectives of dense retrieval and generative retrieval, we propose utilizing dense retrieval to provide relevance feedback for generative retrieval. Under an alternate training framework, the generative retrieval model gradually acquires higher-quality ranking signals to optimize the model. Experimental results show that our approach increasing Recall@1 by 12.9% with respect to the baselines on MS MARCO dataset.

Within online platforms, it is critical to capture the dynamic user preference from the sequential interaction behaviors for making accurate recommendation over time. Recently, significant progress has been made in sequential recommendation with deep learning. However, existing neural sequential recommender often suffer from the data sparsity issue in real-world applications.

In online industrial advertising systems, conversion actions (e.g., purchases or downloads) often occur significantly delayed, even up to several days or weeks after the user clicks. This phenomenon leads to the crucial challenge calleddelayed feedback problem in streaming CVR prediction, that is, the online systems cannot receive the true label of conversions immediately for continuous training. To mitigate the delayed feedback problem, recent state-of-the-art methods often apply sample duplicate mechanisms to introduce early certain conversion information. Nevertheless, these works have overlooked a crucial issue of rapid shifts in data distribution and considered both the newly observed data and duplicated early data together, resulting in biases in both distributions. In this work, we propose a Dually enhanced Delayed Feedback Model (DDFM), which tackles the above issues by treating the newly observed data and duplicated early data separately. DDFM consists of dual unbiased CVR estimators that share the same form but utilize different latent variables as weights: one for the newly observed data and the other for the duplicated early data. To avoid high variance, we adopt an addition-only formula for these latent variables, eliminating multiplication or division operations. Furthermore, we design a shared-bottom network that efficiently and jointly estimates the latent variables in DDFM. Theoretical analysis demonstrates the unbiasedness and convergence properties of DDFM. Extensive experiments on both public and industrial large-scale real-world datasets exhibit that our proposed DDFM consistently outperforms existing state-of-the-art methods.

Document retrieval has always been a crucial problem in Web search. Recent works leverage pre-trained language models to represent documents in dense vectors. However, these works focus on the textual content but ignore the appearance of web pages (e.g., the visual style, the layout, and the images), which are actually essential for information delivery. To alleviate this problem, we propose a new dense retrieval model, namely VILE, to incorporate visual features into document representations. However, because a web page is usually very large and contains diverse information, simply concatenating its textual and visual features may result in a cluttered multi-modal representation that lacks focus on the important parts of the page. We observe that web pages often have a structured content organization, comprising multiple blocks that convey different information. Motivated by the observation, we propose building a multi-modal document representation by aggregating the fine-grained multi-modal block representations, to enable a more comprehensive understanding of the page. Specifically, we first segment a web page into multiple blocks, then create multi-modal features for each block. %allowing for more effective capture of its content and visual information. The representations of all blocks are then integrated into the final multi-modal page representation. VILE can better model the importance of different content regions, leading to a high-quality multi-modal representation. We collect screenshots and the corresponding layout information of some web pages in the MS MARCO Document Ranking dataset, resulting in a new multi-modal document retrieval dataset. Experimental results conducted on this dataset demonstrate that our model exhibits significant improvements over existing document retrieval models. Our code is available at https://github.com/yhy-2000/VILE.

The key of sequential recommendation lies in the accurate item correlation modeling. Previous models infer such information based on item co-occurrences, which may fail to capture the real causal relations, and impact the recommendation performance and explainability. In this paper, we equip sequential recommendation with a novel causal discovery module to capture causalities among user behaviors. Our general idea is firstly assuming a causal graph underlying item correlations, and then we learn the causal graph jointly with the sequential recommender model by fitting the real user behavior data. More specifically, in order to satisfy the causality requirement, the causal graph is regularized by a differentiable directed acyclic constraint. Considering that the number of items in recommender systems can be very large, we represent different items with a unified set of latent clusters, and the causal graph is defined on the cluster level, which enhances the model scalability and robustness. In addition, we provide theoretical analysis on the identifiability of the learned causal graph. To the best of our knowledge, this paper makes a first step towards combining sequential recommendation with causal discovery. For evaluating the recommendation performance, we implement our framework with different neural sequential architectures, and compare them with many state-of-the-art methods based on real-world datasets. Empirical studies manifest that our model can on average improve the performance by about 6.1% and 11.3% on F1and NDCG, respectively. To evaluate the model explainability, we build a new dataset with human labeled explanations for both quantitative and qualitative analysis.

Active learning is effective for tasks with limited labeled data by annotating a small set of data actively. It utilizes the current trained model to evaluate all unlabeled samples and annotates the best samples scored by a specific query strategy to update the underlying model iteratively. Most active learning approaches rely on only the current evaluation score but ignore the results from previous iterations. In this paper, we propose using more historical evaluation results which can provide additional information to help better select samples. First, we apply two heuristic features of the historical evaluation results, i.e. the weighted sum and the fluctuation of history sequences. Next, to make fuller use of the information contained in the historical results, we design a query strategy that learns to select samples based on the history sequence automatically. Our proposed idea is general and can be combined with both basic and state-of-the-art query strategies to achieve improvements. Experimental results show that our methods significantly promote existing methods.

Providing personalization in product search has attracted increasing attention in both industry and research communities. Most existing personalized product search methods model users' individual search interests based on their historical search logs to generate personalized search results. However, the search logs may be sparse or noisy in the real scenario, which is difficult for existing methods to learn accurate and robust user representations. To address this issue, we propose a contrastive learning framework CoPPS that aims to learn high-quality user representations for personalized product search. Specifically, we design three data augmentation and contrastive learning strategies to construct self-supervision signals from the original search behaviours. The contrastive learning tasks utilize an external knowledge graph and exploit the correlations within and between user sequences, thereby facilitating the discovery of more meaningful search patterns and ultimately enhancing the quality of personalized search. Experimental results on the public Amazon datasets verify the effectiveness of our approach.

Recently, pretrained models have achieved remarkable performance not only in natural language processing but also in information retrieval (IR). Previous studies show that IR-oriented pretraining tasks can achieve better performance than only finetuning pretrained language models in IR datasets. Besides, the massive search log data obtained from mainstream search engines can be used in IR pretraining, for it contains users' implicit judgments of document relevance under a concrete query. However, existing methods mainly use direct query-document click signals to pretrain models. The potential supervision signals from search logs are far from being well explored. In this paper, we propose to comprehensively leverage four query-document relevance relations, including co-interaction and multi-hop relations, to pretrain ranking models in IR. Specifically, we focus on the user's click behavior and construct an Interaction Graph to represent the global relevance relations between queries and documents from all search logs. With the graph, we can consider the co-interaction and multi-hop q-d relationships through their neighbor nodes. Based on the relations extracted from the interaction graph, we propose four strategies to generate contrastive positive and negative q-d pairs and use these data to pretrain ranking models. Experimental results on both industrial and academic datasets demonstrate the effectiveness of our method.

Conversational recommender systems~(CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data.

Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver, suffering from high cost for multi-task deployment (e.g. a model copy for a task) and inferior performance on complex mathematical problems in practical applications. To address these issues, we propose JiuZhang 2.0, a unified Chinese PLM specially for multi-task mathematical problem solving. Our idea is to maintain a moderate-sized model and employ the cross-task knowledge sharing to improve the model capacity in a multi-task setting. Specially, we construct a Mixture-of-Experts (MoE) architecture for modeling mathematical text, to capture the common mathematical knowledge across tasks. For optimizing the MoE architecture, we design multi-task continual pre-training and multi-task fine-tuning strategies for multi-task adaptation. These training strategies can effectively decompose the knowledge from the task data and establish the cross-task sharing via expert networks. To further improve the general capacity of solving different complex tasks, we leverage large language models (LLMs) as complementary models to iteratively refine the generated solution by our PLM, via in-context learning. Extensive experiments have demonstrated the effectiveness of our model.

Next-basket Recommendation (NBR) refers to the task of predicting a set of items that a user will purchase in the next basket. However, most of existing works merely focus on the correlations between user preferences and predicted items, ignoring the essential correlations among items in the next basket, which often results in over-homogenization of predicted items. In this work, we presents a Generative next-basket Recommendation model (GenRec), a novel NBR paradigm that generates the recommended items one by one to form the next basket via an autoregressive decoder. This generative NBR paradigm contributes to capturing and considering item correlations inside each baskets in both training and serving. Moreover, we jointly consider user’s both item- and basket-level contextual information to better capture user’s multi-granularity preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model.

Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are very crucial. However, existing CRS datasets suffer from the long-tail issue, i.e., a large proportion of items are rarely (or even never) mentioned in the conversations, which are called long-tail items. As a result, the CRSs trained on these datasets tend to recommend frequent items, and the diversity of the recommended items would be largely reduced, making users easier to get bored.

In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users’ interests but also by other factors, such as duration bias and noisy watching. Duration bias refers to the tendency for users to spend more time on videos with longer durations, regardless of their actual interest level. Noisy watching, on the other hand, describes users taking time to determine whether they like a video or not, which can result in users spending time watching videos they do not like. Consequently, the existence of duration bias and noisy watching make watch time an inadequate label for indicating user interest. Furthermore, current methods primarily address duration bias and ignore the impact of noisy watching, which may limit their effectiveness in uncovering user interest from watch time. In this study, we first analyze the generation mechanism of users’ watch time from a unified causal viewpoint. Specifically, we considered the watch time as a mixture of the user’s actual interest level, the duration-biased watch time, and the noisy watch time. To mitigate both the duration bias and noisy watching, we propose Debiased and Denoised watch time Correction (D2Co), which can be divided into two steps: First, we employ a duration-wise Gaussian Mixture Model plus frequency-weighted moving average for estimating the bias and noise terms; then we utilize a sensitivity-controlled correction function to separate the user interest from the watch time, which is robust to the estimation error of bias and noise terms. The experiments on two public video recommendation datasets and online A/B testing indicate the effectiveness of the proposed method.

Modern online service providers such as online shopping platforms often provide both search and recommendation (S&R) services to meet different user needs. Rarely has there been any effective means of incorporating user behavior data from both S&R services. Most existing approaches either simply treat S&R behaviors separately, or jointly optimize them by aggregating data from both services, ignoring the fact that user intents in S&R can be distinctively different. In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users' search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors. Specifically, SESRec first aligns query and item embeddings based on users' query-item interactions for the computations of their similarities. Two transformer encoders are used to learn the contextual representations of S&R behaviors independently. Then a contrastive learning task is designed to supervise the disentanglement of similar and dissimilar representations from behavior sequences of S&R. Finally, we extract user interests by the attention mechanism from three perspectives, i.e., the contextual representations, the two separated behaviors containing similar and dissimilar interests. Extensive experiments on both industrial and public datasets demonstrate that SESRec consistently outperforms state-of-the-art models. Empirical studies further validate that SESRec successfully disentangle similar and dissimilar user interests from their S&R behaviors.

Legal case matching, which automatically constructs a model to estimate the similarities between the source and target cases, has played an essential role in intelligent legal systems. Semantic text matching models have been applied to the task where the source and target legal cases are considered as long-form text documents. These general-purpose matching models make the predictions solely based on the texts in the legal cases, overlooking the essential role of the law articles in legal case matching. In the real world, the matching results (e.g., relevance labels) are dramatically affected by the law articles because the contents and the judgments of a legal case are radically formed on the basis of law. From the causal sense, a matching decision is affected by the mediation effect from the cited law articles by the legal cases, and the direct effect of the key circumstances (e.g., detailed fact descriptions) in the legal cases. In light of the observation, this paper proposes a model-agnostic causal learning framework called Law-Match, under which the legal case matching models are learned by respecting the corresponding law articles. Given a pair of legal cases and the related law articles, Law-Match considers the embeddings of the law articles as instrumental variables(IVs), and the embeddings of legal cases as treatments. Using IV regression, the treatments can be decomposed into law-related and law-unrelated parts, respectively reflecting the mediation and direct effects. These two parts are then combined with different weights to collectively support the final matching prediction. We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models. Comprehensive experiments show that Law-Match can outperform state-of-the-art baselines on three public datasets.

Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless interactions from them. Although they reduce the high computational costs caused by the exponential growth of high-order feature combinations, they still suffer from the degradation of model capability due to the suboptimal learning of the restricted feature orders. The solution to maintain the model capability and meanwhile keep it efficient is a technical challenge, which has not been adequately addressed. To address this issue, we propose an adaptive feature interaction learning model, named as EulerNet, in which the feature interactions are learned in a complex vector space by conducting space mapping according to Euler's formula. EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way. Furthermore, EulerNet incorporates the implicit and explicit feature interactions into a unified architecture, which achieves the mutual enhancement and largely boosts the model capabilities. Such a network can be fully learned from data, with no need of pre-designed form or order for feature interactions. Extensive experiments conducted on three public datasets have demonstrated the effectiveness and efficiency of our approach. Our code is available at: https://github.com/RUCAIBox/EulerNet.

In recent years, the reproducibility of recommendation models has become a severe concern in recommender systems. In light of this challenge, we have previously released a unified, comprehensive and efficient recommendation library called RecBole, attracting much attention from the research community. With the increasing number of users, we have received a number of suggestions and update requests. This motivates us to make further improvements on our library, so as to meet the user requirements and contribute to the research community. In this paper, we present a significant update of RecBole, making it more user-friendly and easy-to-use as a comprehensive benchmark library for recommendation. More specifically, the highlights of this update are summarized as: (1) we include more benchmark models and datasets, improve the benchmark framework in terms of data processing, training and evaluation, and release reproducible configurations to benchmark the recommendation models; (2) we upgrade the user friendliness of our library by providing more detailed documentation and well-organized frequently asked questions, and (3) we propose several development guidelines for the open-source library developers. These extensions make it much easier to reproduce the benchmark results and stay up-to-date with the recent advances on recommender systems. Our update is released at the link: https://github.com/RUCAIBox/RecBole.

With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely increases, which limits the use of high-order interaction models in real industrial applications. Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference. However, they suffer from the degradation of model accuracy in knowledge distillation process. It is challenging to balance the efficiency and effectiveness of the shallow student models. To address this problem, we propose a Directed Acyclic Graph Factorization Machine (KD-DAGFM) to learn the high-order feature interactions from existing complex interaction models for CTR prediction via Knowledge Distillation. The proposed lightweight student model DAGFM can learn arbitrary explicit feature interactions from teacher networks, which achieves approximately lossless performance and is proved by a dynamic programming algorithm. Besides, an improved general model KD-DAGFM+ is shown to be effective in distilling both explicit and implicit feature interactions from any complex teacher model. Extensive experiments are conducted on four real-world datasets, including a large-scale industrial dataset from WeChat platform with billions of feature dimensions. KD-DAGFM achieves the best performance with less than 21.5% FLOPs of the state-of-the-art method on both online and offline experiments, showing the superiority of DAGFM to deal with the industrial scale data in CTR prediction task.

Alleviating the examination and trust bias in ranking systems is an important research line in unbiased learning-to-rank (ULTR). Current methods typically use the propensity to correct the biased user clicks and then learn ranking models based on the corrected clicks. Though successes have been achieved, directly modifying the clicks suffers from the inherent high variance because the propensities are usually involved in the denominators of corrected clicks. The problem gets even worse in the situation of mixed examination and trust bias. To address the issue, this paper proposes a novel ULTR method called Decomposed Ranking Debiasing (DRD). DRD is tailored for learning unbiased relevance models with low variance in the existence of examination and trust bias. Unlike existing methods that directly modify the original user clicks, DRD proposes to decompose each click prediction as the combination of a relevance term outputted by the ranking model and other bias terms. The unbiased relevance model, therefore, can be learned by fitting the overall click predictions to the biased user clicks. A joint learning algorithm is developed to learn the relevance and bias models' parameters alternatively. Theoretical analysis showed that, compared with existing methods, DRD has lower variance while retains unbiasedness. Empirical studies indicated that DRD can effectively reduce the variance and outperform the state-of-the-art ULTR baselines.

The key to personalized search is modeling user intents to tailor returned results for different users. Existing personalized methods mainly focus on learning implicit user interest vectors. In this paper, we propose ExpliPS, a personalized search model that explicitly incorporates query subtopics into personalization. It models the user’s current intent by estimating the user’s preference over the subtopics of the current query and personalizes the results over the weighted subtopics. We think that in such a way, personalized search could be more explainable and stable. Specifically, we first employ a semantic encoder to learn the representations of the user’s historical behaviours. Then with the historical behaviour representations, a subtopic preference encoder is devised to predict the user’s subtopic preferences on the current query. Finally, we rerank the candidates via a subtopic-aware ranker that prioritizes the documents relevant to the user-preferred subtopics. Experimental results show our model ExpliPS outperforms the state-of-the-art personalized web search models with explainable and stable results.

Deep semantic matching aims at discriminating the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents with a graph structure, then leverage both the intrinsic document features and the extrinsic neighbor features to derive discrimination. Most of the existing works mainly care about how to utilize the presented neighbors, whereas limited effort is made to filter appropriate neighbors. We argue that the neighbor features could be highly noisy and partially useful. Thus, a lack of effective neighbor selection will not only incur a great deal of unnecessary computation cost but also restrict the matching accuracy severely. In this work, we propose a novel framework, C ascaded D eep S emantic M atching ( CDSM ), for accurate and efficient semantic matching on textual graphs. CDSM is highlighted for its two-stage workflow. In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost. We design both one-step and multi-step selection methods. In the second stage, a high-capacity graph-based matching network is employed to compute fine-grained relevance scores based on the well-selected neighbors. It is worth noting that CDSM is a generic framework which accommodates most of the mainstream graph-based semantic matching networks. The major challenge is how the selector can learn to discriminate the neighbors’ usefulness which has no explicit labels. To cope with this problem, we design a weak-supervision strategy for optimization, where we train the graph-based matching network at first and then the ad-hoc neighbor selector is learned on top of the annotations from the matching network. We conduct extensive experiments with three large-scale datasets, showing that CDSM notably improves the semantic matching accuracy and efficiency thanks to the selection of high-quality neighbors. The source code is released at https://github.com/jingjyyao/CDSM.

Sequential recommendation has recently attracted increasing attention from the industry and academic communities. While previous models have achieved remarkable successes, an important problem may still hinder their performances, that is, the sparsity of the real-world data. In this paper, we propose a novel counterfactual data augmentation framework to alleviate the problem of data sparsity. In specific, our framework contains a sampler model and an anchor model. The sampler model aims to generate high-quality user behavior sequences, while the anchor model is trained based on the original and new generated samples, and leveraged to provide the final recommendation list. To implement the sampler model, we first design four types of heuristic methods based on either random or frequency-based strategies. And then, to improve the quality of the generated sequences, we propose two learning-based samplers by discovering the decision boundaries or increasing the sample informativeness. At last, we build an RL based model to automatically determine where to edit the history behaviors and how many items should be replaced. Considering that the sampler model can be imperfect, we, at last, analyze the influence of the noisy information contained in the generated sequences on the anchor model in theory, and design a simple but effective method to better serve the anchor model. We conduct extensive experiments to demonstrate the effectiveness of our model.

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performances on complex questions are still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances in KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and relevant background. Then, we present two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. Specifically, we illustrate their procedures with flow designs and discuss their difference and similarity. Next, we summarize the challenges that these two categories of methods encounter when answering complex questions, and explicate advanced solutions as well as techniques used in existing work. After that, we discuss the potential impact of pre-trained language models (PLMs) on complex KBQA. To help readers catch up with SOTA methods, we also provide a comprehensive evaluation and resource about complex KBQA task. Finally, we conclude and discuss several promising directions related to complex KBQA for future research.

While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namelyconsistency and complementarity network(CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet.

The goal of personalized search is to tailor the document ranking list to meet user's individual needs. Previous studies showed users usually look for the information that has been searched before. This is called re-finding behavior which is widely explored in existing personalized search approaches. However, most existing methods for identifying re-finding behavior focus on simple lexical similarities between queries. In this paper, we propose a personalized framework based on hierarchical memory networks (MN) to enhance the identification of the potential re-finding behavior. Specifically, we explore the potential re-finding behaviors of users from two dimensions. (1) Granularity dimension. The framework carries out re-finding identification with external memories from word, sentence, and session levels. (2) Query intent dimension. Query-based re-finding and document-based re-finding are taken into account to cover user's different query intents. To enhance the interaction between different memory slots, we optimize the$READ$operation of MN with two strategies that utilize the information in memory in a multi-hop way. Endowed with these memory networks, we can enhance user's potential re-finding behaviors and build a fine-grained user model dynamically. Experimental results on two datasets have a significant improvement over baselines, and the optimized$READ$operation shows better performance.

Recent studies show that historical behaviors (such as queries and their clicks) contained in a search session can benefit the ranking performance of subsequent queries in the session. Existing neural context-aware ranking models usually rank documents based on either latent representations of user search behaviors or the word-level interactions between the candidate document and each historical behavior in the search session. However, these two kinds of models both have their own drawbacks. Representation-based models neglect fine-grained information on word-level interactions, whereas interaction-based models suffer from the length restriction of session sequence because of the large cost of word-level interactions. To complement the limitations of these two kinds of models, we propose a unified context-aware document ranking model that takes full advantage of both representation and interaction. Specifically, instead of matching a candidate document with every single historical query in a session, we encode the session history into a latent representation and use this representation to enhance the current query and the candidate document. We then just match the enhanced query and candidate document with several matching components to capture the fine-grained information of word-level interactions. Rich experiments on two public query logs prove the effectiveness and efficiency of our model for leveraging representation and interaction.

Search result diversification aims to generate diversified search results so as to meet the various information needs of users. Most of those existing diversification methods greedily select the optimal documents one-by-one comparing with the selected document sequences. Due to the fact that the information utilities of the candidate documents are not independent, a model based on greedy document selection may not produce the global optimal ranking results. To address this issue, some work proposes to model global document interactions regardless of whether a document is selected, which is inconsistent with actual user behavior. In this article, we propose a new supervised diversification framework as an ensemble of global interaction and document selection. Based on a self-attention encoder-decoder structure and an RNN-based document selection component, the model can simultaneously leverage both the global interactions among all the documents and the interactions between the selected sequence and each unselected document. This framework is called Greedy Diversity Encoder with Self-Attention (GDESA). Experimental results show that GDESA outperforms previous methods that rely just on global interactions, and our further analysis demonstrates that using both global interactions and document selection is necessary and beneficial.

Recommender systems are currently widely used in various applications helping people filter information. Existing models always embed the rich information for recommendation, such as items, users, and contexts in real-value vectors, and make predictions based on these vectors. In the view of causal inference, the associations between representation vectors and user feedback are inevitably a mixture of the causal part that describes why a user prefers an item, and the non-causal part that merely reflects the statistical dependencies, for example, the display ranking position and sales promotion. However, most recommender systems assume the user-item interactions are only affected by user preferences, neglecting the striking differences between these two associations. To address this problem, we propose a model-agnostic causal learning framework called IV4Rec+ that can effectively decompose the embedding vectors into these two parts. Moreover, two strategies are proposed to utilize search queries as instrumental variables: IV4Rec+(I) only decomposes the item embeddings, while IV4Rec+(UI) decomposes both user and item embeddings. IV4Rec+ is a model-agnostic design that can be applied to many existing recommender systems, e.g., DIN, NRHUB, and SRGNN. Extensive experiments on three datasets show that IV4Rec+ significantly facilitates the performance of recommender systems and outperforms state-of-the-art frameworks.

To characterize complex and heterogeneous side information in recommender systems, the heterogeneous information network (HIN) has shown superior performance and attracted much research attention. In HIN, the rich entities, relations, and paths can be utilized to model the correlations of users and items; such a task setting is often calledHIN-based recommendation. Although HIN provides a general approach to modeling rich side information, it lacks special consideration on the goal of the recommendation task. The aggregated context from the heterogeneous graph is likely to incorporate irrelevant information, and the learned representations are not specifically optimized according to the recommendation task. Therefore, there is a need to rethink how to leverage the useful information from HIN to accomplish the recommendation task. To address the above issues, we propose a Curriculum pre-training based HEterogeneous Subgraph Transformer (calledCHEST) with newdata characterization,representation model,andlearning algorithm. Specifically, we consider extracting useful information from HIN to compose the interaction-specific heterogeneous subgraph, containing highly relevant context information for recommendation. Then, we capture the rich semantics (e.g., graph structure and path semantics) within the subgraph via a heterogeneous subgraph Transformer, where we encode the subgraph into multi-slot sequence representations. Besides, we design a curriculum pre-training strategy to provide an elementary-to-advanced learning process. The elementary course focuses on capturing local context information within the subgraph, and the advanced course aims to learn global context information. In this way, we gradually capture useful semantic information from HIN for modeling user-item interactions. Extensive experiments conducted on four real-world datasets demonstrate the superiority of our proposed method over a number of competitive baselines, especially when only limited training data is available.

Legal judgment prediction (LJP) is a fundamental task of legal artificial intelligence. It aims to automatically predict the judgment results of legal cases. Three typical subtasks are relevant law article prediction, charge prediction, and term-of-penalty prediction. Due to the wide range of potential applications, LJP has attracted a great deal of interest, prompting the development of numerous approaches. These methods mainly focus on building a more accurate representation of a case’s fact description in order to improve the performance of judgment prediction. They overlook, however, the practical judicial scenario in which human judges often compare similar law articles or possible charges before making a final decision. To this end, we propose a supervised contrastive learning framework for the LJP task. Specifically, we train the model to distinguish (1) various law articles within the same chapter of a Law and (2) similar charges of the same law article or related law articles. By this means, the fine-grained differences between similar articles/charges can be captured, which are important for making a judgment. Besides, we optimize our model by identifying cases with the same article/charge labels, allowing it to more effectively model the relationship between the case’s fact description and its associated labels. By jointly learning the LJP task with the aforementioned contrastive learning tasks, our model achieves better performance than the state-of-the-art models on two real-world datasets.

In recommender systems, top- N recommendation is an important task with implicit feedback data. Although the recent success of deep learning largely pushes forward the research on top- N recommendation, there are increasing concerns on appropriate evaluation of recommendation algorithms. It therefore is important to study how recommendation algorithms can be reliably evaluated and thoroughly verified. This work presents a large-scale, systematic study on six important factors from three aspects for evaluating recommender systems. We carefully select 12 top- N recommendation algorithms and eight recommendation datasets. Our experiments are carefully designed and extensively conducted with these algorithms and datasets. In particular, all the experiments in our work are implemented based on an open sourced recommendation library, Recbole [ 139 ], which ensures the reproducibility and reliability of our results. Based on the large-scale experiments and detailed analysis, we derive several key findings on the experimental settings for evaluating recommender systems. Our findings show that some settings can lead to substantial or significant differences in performance ranking of the compared algorithms. In response to recent evaluation concerns, we also provide several suggested settings that are specially important for performance comparison.

Sequential recommendation models aim to predict the interested items to a user based on his historical behaviors. To train sequential recommenders, implicit feedback data is widely adopted since it is easier to obtain than explicit feedback data. In the setting of implicit feedback, a user’s historical behaviors can be characterized as a chronologically ordered sequence of interacted items. From a perspective of machine learning, the historical interaction sequence and the recommended items can be considered as context and label , respectively, which are usually in one-hot representations in the recommendation models. However, due to the discrete nature, one-hot representations are hard to sufficiently reflect the underlying user preference, and might also contain noise from implicit feedback that will mislead the model training. To solve these issues, we propose a general optimization framework, Multi-View Smoothness (MVS), to enhance the smoothness of sequential recommendation models in both data representations and model learning. Specifically, with the help of a complementary model, we smooth and enrich the one-hot representations of contexts and labels to better depict the underlying user preference (i.e., context smoothness and label smoothness), and devise a model regularization strategy to enforce the neighborhood smoothness of the model itself (i.e., model smoothness). Based on these strategies, we design three regularizers to constrain and improve the training of sequential recommendation models. Extensive experiments on five datasets show that our approach is able to improve the performance of various base models consistently and outperform other regularization training methods.

Within online platforms, it is critical to capture the semantics of sequential user behaviors for accurately predicting user interests. Recently, significant progress has been made in sequential recommendation with deep learning. However, existing neural sequential recommendation models may not perform well in practice due to the sparsity of the real-world data especially in cold-start scenarios. To tackle this problem, we propose the model ReDA, which stands for Retrieval-enhanced Data Augmentation for modeling sequential user behaviors. The main idea of our approach is to leverage the related information from similar users for generating both relevant and diverse augmentation. First, we train a neural retriever to retrieve the augmentation users according to the se- mantic similarity between user representations, and then conduct two types of data augmentation to generate augmented user representations. Furthermore, these augmented data are incorporated in a contrastive learning framework for learning more capable representations. Extensive experiments conducted on both public and industry datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available.

Users' search tasks have become increasingly complicated, requiring multiple queries and interactions with the results. Recent studies have demonstrated that modeling the historical user behaviors in a session can help understand the current search intent. Existing context-aware ranking models primarily encode the current session sequence (from the first behavior to the current query) and compute the ranking score using the high-level representations. However, there is usually some noise in the current session sequence (useless behaviors for inferring the search intent) that may affect the quality of the encoded representations. To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query. Specifically, we design three generative tasks that can help the encoder to infer the actual search intent: (1) predicting future queries, (2) predicting future clicked documents, and (3) predicting a supplemental query. We jointly learn the ranking task with these generative tasks using an encoder-decoder structured approach. Extensive experiments on two public search logs demonstrate that our model outperforms all existing baselines, and the designed generative tasks can actually help the ranking task. Besides, additional experiments also show that our approach can be easily applied to various Transformer-based encoder-decoder models and improve their performance.

Sequential recommender systems train their models based on a large amount of implicit user feedback data and may be subject to biases when users are systematically under/over-exposed to certain items. Unbiased learning based on inverse propensity scores (IPS), which estimate the probability of observing a user-item pair given the historical information, has been proposed to address the issue. In these methods, propensity score estimation is usually limited to the view of item, that is, treating the feedback data as sequences of items that interacted with the users. However, the feedback data can also be treated from the view of user, as the sequences of users that interact with the items. Moreover, the two views can jointly enhance the propensity score estimation. Inspired by the observation, we propose to estimate the propensity scores from the views of user and item, called Dually Enhanced Propensity Score Estimation (DEPS). Specifically, given a target user-item pair and the corresponding item and user interaction sequences, DEPS first constructs a time-aware causal graph to represent the user-item observational probability. According to the graph, two complementary propensity scores are estimated from the views of item and user, respectively, based on the same set of user feedback data. Finally, two transformers are designed to make use of the two propensity scores and make the final preference prediction. Theoretical analysis showed the unbiasedness and variance of DEPS. Experimental results on three publicly available benchmarks and a proprietary industrial dataset demonstrated that DEPS can significantly outperform the state-of-the-art baselines.

In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures. First of all, from a data perspective, we consider three important topics related to data issues (ie sparsity, bias and distribution shift ), and develop five packages accordingly, including meta-learning, data augmentation, debiasing, fairness and cross-domain recommendation. Furthermore, from a model perspective, we develop two benchmarking packages for Transformer-based and graph neural network~(GNN)-based models, respectively. All the packages (consisting of 65 new models) are developed based on a popular recommendation framework RecBole, ensuring that both the implementation and interface are unified. For each package, we provide complete implementations from data loading, experimental setup, evaluation and algorithm implementation. This library provides a valuable resource to facilitate the up-to-date research in recommender systems. The project is released at the link: \urlhttps://github.com/RUCAIBox/RecBole2.0.

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehensively understand the privacy protection issues, we first introduce the concept of attackable representation to systematically characterize the privacy attacks that each model can face. Inspired by the qualitative results, we propose a novel P rivacy-P reserving neural G raph M atching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. Instead, we learn multi-perspective graph representations based on learnable context vectors. To alleviate the attacks to graph properties, the obfuscated features that contain information from both graphs are communicated. In this way, the private properties of each graph can be difficult to infer. Based on the node-graph matching techniques while calculating the obfuscated features, PPGM can also be effective in similarity measuring. To quantitatively evaluate the privacy-preserving ability of neural GSL models, we further propose an evaluation protocol via training supervised black-box attack models. Extensive experiments on widely-used benchmarks show the effectiveness and strong privacy-protection ability of the proposed model PPGM. The code is available at: https://github.com/RUCAIBox/PPGM.

In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL methods rely on explicit item IDs for developing the sequence models to better capture user preference. Though effective to some extent, these methods are difficult to be transferred to new recommendation scenarios, due to the limitation by explicitly modeling item IDs. To tackle this issue, we present a novel universal sequence representation learning approach, named UniSRec. The proposed approach utilizes the associated description text of items to learn transferable representations across different recommendation scenarios. For learning universal item representations, we design a lightweight item encoding architecture based on parametric whitening and mixture-of-experts enhanced adaptor. For learning universal sequence representations, we introduce two contrastive pre-training tasks by sampling multi-domain negatives. With the pre-trained universal sequence representation model, our approach can be effectively transferred to new recommendation domains or platforms in a parameter-efficient way, under either inductive or transductive settings. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the proposed approach. Especially, our approach also leads to a performance improvement in a cross-platform setting, showing the strong transferability of the proposed universal SRL method. The code and pre-trained model are available at: https://github.com/RUCAIBox/UniSRec.

Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e.g., click, like and purchase). Besides considering the relevance between recommendations and trigger item, the recommendations should also be diversified to avoid information cocoons. However, existing diversified recommendation methods mainly focus on item-level diversity which is insufficient when the recommended items are all relevant to the target item. Moreover, redundant or noisy item features might affect the performance of simple feature-aware recommendation approaches. Faced with these issues, we propose a Feature Disentanglement Self-Balancing Re-ranking framework (FDSB) to capture feature- aware diversity. The framework consists of two major modules, namely disentangled attention encoder (DAE) and self-balanced multi-aspect ranker. In DAE, we use multi-head attention to learn disentangled aspects from rich item features. In the ranker, we develop an aspect-specific ranking mechanism that is able to adaptively balance the relevance and diversity for each aspect. In experiments, we conduct offline evaluation on the collected dataset and deploy FDSB on KuaiShou app for online ??/?? test on the function of relevant recommendation. The significant improvements on both recommendation quality and user experience verify the effectiveness of our approach.

Search result diversification focuses on reducing redundancy and improving subtopic richness in the results for a given query. Most existing approaches measure document diversity mainly based on text or pre-trained representations. However, some underlying relationships between the query and documents are difficult for the model to capture only from the content. Given that the knowledge base can offer well-defined entities and explicit relationships between entities, we exploit knowledge to model the relationship between documents and the query and propose a knowledge-enhanced search result diversification approach KEDIV. Concretely, we build a query-specific relation graph to model the complicated query-document relationship from an entity view. Then a graph neural network and node weight adjust algorithm are applied to the relation graph to obtain context-aware entity representations and document representations at each selection step. The diversity features are derived from the updated node representations of the relation graph. In this way, we can take advantage of entities' abundant information to model document's diversity in search result diversification. Experimental results on commonly used datasets show that our proposed approach can outperform the state-of-the-art methods.

Conversational recommender systems (CRS) aim to proactively elicit user preference and recommend high-quality items through natural language conversations. Typically, a CRS consists of a recommendation module to predict preferred items for users and a conversation module to generate appropriate responses. To develop an effective CRS, it is essential to seamlessly integrate the two modules. Existing works either design semantic alignment strategies, or share knowledge resources and representations between the two modules. However, these approaches still rely on different architectures or techniques to develop the two modules, making it difficult for effective module integration. To address this problem, we propose a unified CRS model named UniCRS based on knowledge-enhanced prompt learning. Our approach unifies the recommendation and conversation subtasks into the prompt learning paradigm, and utilizes knowledge-enhanced prompts based on a fixed pre-trained language model (PLM) to fulfill both subtasks in a unified approach. In the prompt design, we include fused knowledge representations, task-specific soft tokens, and the dialogue context, which can provide sufficient contextual information to adapt the PLM for the CRS task. Besides, for the recommendation subtask, we also incorporate the generated response template as an important part of the prompt, to enhance the information interaction between the two subtasks. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach. Our code is publicly available at the link: https://github.com/RUCAIBox/UniCRS.

In streaming media applications, like music Apps, songs are recommended in a continuous way in users' daily life. The recommended songs are played automatically although users may not pay any attention to them, posing a challenge of user attention bias in training recommendation models, i.e., the training instances contain a large number of false-positive labels (users' feedback). Existing approaches either directly use the auto-feedbacks or heuristically delete the potential false-positive labels. Both of the approaches lead to biased results because the false-positive labels cause the shift of training data distribution, hurting the accuracy of the recommendation models. In this paper, we propose a learning-based counterfactual approach to adjusting the user auto-feedbacks and learning the recommendation models using Neural Dueling Bandit algorithm, called NDB. Specifically, NDB maintains two neural networks: a user attention network for computing the importance weights that are used for modifying the original rewards, and another random network trained with dueling bandit for conducting online recommendations based on the modified rewards. Theoretical analysis showed that the modified rewards are statistically unbiased, and the learned bandit policy enjoys a sub-linear regret bound. Experimental results demonstrated that NDB can significantly outperform the state-of-the-art baselines.

This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model (PLM) for effectively understanding and representing mathematical problems. Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement. Typically, it requires complex mathematical logic and background knowledge for solving mathematical problems.

Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dual-perspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach. Our code is available at https://github.com/RUCAIBox/DPGNN .

Recently, graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. However, when adapted to downstream tasks, it usually requires abundant task-specific labeled data, which can be extremely scarce in practice. A promising solution to data scarcity is to pre-train a transferable and expressive GNN model on large amounts of unlabeled graphs or coarse-grained labeled graphs. Then the pre-trained GNN is fine-tuned on downstream datasets with task-specific fine-grained labels. In this paper, we present a novel Graph Matching based GNN Pre-Training framework, called GMPT. Focusing on a pair of graphs, we propose to learn structural correspondences between them via neural graph matching, consisting of both intra-graph message passing and inter-graph message passing. In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training task. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training. We further propose an approximate contrastive training strategy to significantly reduce time/memory consumption. Extensive experiments on multi-domain, out-of-distribution benchmarks have demonstrated the effectiveness of our approach. The code is available at: https://github.com/RUCAIBox/GMPT.

Limited by the statistical-based machine learning framework, a spurious correlation is likely to appear in existing knowledge-aware recommendation methods. It refers to a knowledge fact that appears causal to the user behaviors (inferred by the recommender) but is not in fact. For tackling this issue, we present a novel approach to discovering and alleviating the potential spurious correlations from a counterfactual perspective. To be specific, our approach consists of two counterfactual generators and a recommender. The counterfactual generators are designed to generate counterfactual interactions via reinforcement learning, while the recommender is implemented with two different graph neural networks to aggregate the information from KG and user-item interactions respectively. The counterfactual generators and recommender are integrated in a mutually collaborative way. With this approach, the recommender helps the counterfactual generators better identify potential spurious correlations and generate high-quality counterfactual interactions, while the counterfactual generators help the recommender weaken the influence of the potential spurious correlations simultaneously. Extensive experiments on three real-world datasets have shown the effectiveness of the proposed approach by comparing it with a number of competitive baselines. Our implementation code is available at: https://github.com/RUCAIBox/CGKR.

As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications --- the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely IOT-Match, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. Different from most existing methods, which merely focus on the sentence-level semantic similarity between legal cases, our IOT-Match learns to extract rationales from paired legal cases based on both semantics and legal characteristics of their sentences. The extracted rationales are further applied to generate faithful explanations and conduct matching. Moreover, the proposed IOT-Match is robust to the alignment label insufficiency issue commonly in practical legal case matching tasks, which is suitable for both supervised and semi-supervised learning paradigms. To demonstrate the superiority of our IOT-Match method and construct a benchmark of explainable legal case matching task, we not only extend the well-known Challenge of AI in Law (CAIL) dataset but also build a new Explainable Legal cAse Matching (ELAM) dataset, which contains lots of legal cases with detailed and explainable annotations. Experiments on these two datasets show that our IOT-Match outperforms state-of-the-art methods consistently on matching prediction, rationale extraction, and explanation generation.

Asking clarifying questions is an interactive way to effectively clarify user intent. When a user submits a query, the search engine will return a clarifying question with several clickable items of sub-intents for clarification. According to the existing definition, the key to asking high-quality questions is to generate good descriptions for submitted queries and provided items. However, existing methods mainly based on static knowledge bases are difficult to find descriptions for many queries because of the lack of entities within these queries and their corresponding items. For such a query, it is unable to generate an informative question. To alleviate this problem, we propose leveraging top search results of the query to help generate better descriptions because we deem that the top retrieved documents contain rich and relevant contexts of the query. Specifically, we first design a rule-based algorithm to extract description candidates from search results and rank them by various human-designed features. Then, we apply an learning-to-rank model and another generative model for generalization and further improve the quality of clarifying questions. Experimental results show that our proposed methods can generate more readable and informative questions compared with existing methods. The results prove that search results can be utilized to improve users' search experience for search clarification in conversational search systems.

In session search, it's important to utilize historical interactions between users and the search engines to improve document retrieval. However, not all historical information contributes to document ranking. Users often express their preferences in the process of modifying the previous query, which can help us catch useful information in the historical interactions. Inspired by it, we propose to model historical query change to improve document ranking performance. Especially, we characterize multi-granularity query change between each pair of adjacent queries at both term level and semantic level. For term level query change, we calculate three types of term weights, including the retained term weights, added term weights and removed term weights. Then we perform term-based interaction between the candidate document and historical queries based on the term weights. For semantic level query change, we calculate an overall representation of user intent by integrating the representations of each historical query obtained by different types of term weights. Then we adopt representation-based matching between this representation and the candidate document. To improve the effect of query change modeling, we introduce query change classification as an auxiliary task. Experimental results on AOL and TianGong-ST search logs show that our model outperforms most existing models for session search.

Product search has been an important way for people to find products on online shopping platforms. Existing approaches in personalized product search mainly embed user preferences into one single vector. However, this simple strategy easily results in sub-optimal representations, failing to model and disentangle user’s multiple preferences. To overcome this problem, we proposed a category-aware multi-interest model to encode users as multiple preference embeddings to represent user-specific interests. Specifically, we also capture the category indications for each preference to indicate the distribution of categories it focuses on, which is derived from rich relations between users, products, and attributes. Based on these category indications, we develop a category attention mechanism to aggregate these various preference embeddings considering current queries and items as the user’s comprehensive representation. By this means, we can use this representation to calculate matching scores of retrieved items to determine whether they meet the user’s search intent. Besides, we introduce a homogenization regularization term to avoid the redundancy between user interests. Experimental results show that the proposed method significantly outperforms existing approaches.

Ranking has been one of the most important tasks in information retrieval. With the development of deep representation learning, many researchers propose to encode both the query and items into embedding vectors and rank the items according to the inner product or distance measures in the embedding space. However, the ranking models based on vector embeddings may have shortages in effectiveness and efficiency. For effectiveness, they lack the intrinsic ability to model the diversity and uncertainty of queries and items in ranking. For efficiency, nearest neighbor search in a large collection of item vectors can be costly. In this work, we propose to use the recently proposed probabilistic box embeddings for effective and efficient ranking, in which queries and items are parameterized as high-dimensional axis-aligned hyper-rectangles. For effectiveness, we utilize probabilistic box embeddings to model the diversity and uncertainty with the overlapping relations of the hyper-rectangles, and prove that such overlapping measure is a kernel function which can be adopted in other kernel-based methods. For efficiency, we propose a box embedding-based indexing method, which can safely filter irrelevant items and reduce the retrieval latency. We further design a training strategy to increase the proportion of irrelevant items that can be filtered by the index. Experiments on public datasets show that the box embeddings and the box embedding-based indexing approaches are effective and efficient in two ranking tasks: ad hoc retrieval and product recommendation.

Machine-learning based recommender system(RS) has become an effective means to help people automatically discover their interests. Existing models often represent the rich information for recommendation, such as items, users, and contexts, as embedding vectors and leverage them to predict users’ feedback. In the view of causal analysis, the associations between these embedding vectors and users’ feedback are a mixture of the causal part that describes why an item is preferred by a user, and the non-causal part that merely reflects the statistical dependencies between users and items, for example, the exposure mechanism, public opinions, display position, etc. However, existing RSs mostly ignored the striking differences between the causal parts and non-causal parts when using these embedding vectors. In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results. Specifically, we jointly consider users’ behaviors in search scenarios and recommendation scenarios. Adopting the concepts in causal analysis, we embed users’ search behaviors as instrumental variables (IVs), to help decompose original embedding vectors in recommendation, i.e., treatments. IV4Rec then combines the two parts through deep neural networks and uses the combined results for recommendation. IV4Rec is model-agnostic and can be applied to a number of existing RSs such as DIN and NRHUB. Experimental results on both public and proprietary industrial datasets demonstrate that IV4Rec consistently enhances RSs and outperforms a framework that jointly considers search and recommendation.

Sequential recommendation holds the promise of understanding user preference by capturing successive behavior correlations. Existing research focus on designing different models for better fitting the offline datasets. However, the observational data may have been contaminated by the exposure or selection biases, which renders the learned sequential models unreliable. In order to solve this fundamental problem, in this paper, we propose to reformulate the sequential recommendation task with the potential outcome framework, where we are able to clearly understand the data bias mechanism and correct it by re-weighting the training instances with the inverse propensity score (IPS). For more robustness modeling, a clipping strategy is applied to the IPS estimation to reduce the variance of the learning objective. To make our framework more practical, we design a parameterized model to remove the impact of the potential latent confounders. At last, we theoretically analyze the unbiasedness of the proposed framework under both vanilla and clipping IPS estimations. To the best of our knowledge, this is the first work on debiased sequential recommendation. We conduct extensive experiment based on both synthetic and real-world datasets to demonstrate the effectiveness of our framework.

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation. However, in online platforms, logged user behavior data is inevitable to contain noise, and deep recommendation models are easy to overfit on these logged data. To tackle this problem, we borrow the idea of filtering algorithms from signal processing that attenuates the noise in the frequency domain. In our empirical experiments, we find that filtering algorithms can substantially improve representative sequential recommendation models, and integrating simple filtering algorithms (e.g., Band-Stop Filter) with an all-MLP architecture can even outperform competitive Transformer-based models. Motivated by it, we propose FMLP-Rec, an all-MLP model with learnable filters for sequential recommendation task. The all-MLP architecture endows our model with lower time complexity, and the learnable filters can adaptively attenuate the noise information in the frequency domain. Extensive experiments conducted on eight real-world datasets demonstrate the superiority of our proposed method over competitive RNN, CNN, GNN and Transformer-based methods. Our code and data are publicly available at the link: https://github.com/RUCAIBox/FMLP-Rec .

Personalized PageRank (PPR) is a popular node proximity metric in graph mining and network research. A single-source PPR (SSPPR) query asks for the PPR value of each node on the graph. Due to its importance and wide applications, decades of efforts have been devoted to the efficient processing of SSPPR queries. Among existing algorithms, LocalPush is a fundamental method for SSPPR queries and serves as a cornerstone for subsequent algorithms. In LocalPush , a push operation is a crucial primitive operation, which distributes the probability at a node u to ALL u 's neighbors via the corresponding edges. Although this push operation works well on unweighted graphs, unfortunately, it can be rather inefficient on weighted graphs. In particular, on unbalanced weighted graphs where only a few of these edges take the majority of the total weight among them, the push operation would have to distribute "insignificant" probabilities along those edges which just take the minor weights, resulting in expensive overhead. To resolve this issue, in this paper, we propose the EdgePush algorithm, a novel method for computing SSPPR queries on weighted graphs. EdgePush decomposes the aforementioned push operations in edge-based push , allowing the algorithm to operate at the edge level granularity. As a result, it can flexibly distribute the probabilities according to edge weights. Furthermore, our EdgePush allows a fine-grained termination threshold for each individual edge, leading to a superior complexity over LocalPush. Notably, we prove that EdgePush improves the theoretical query cost of LocalPush by an order of up to O ( n ) when the graph's weights are unbalanced. Our experimental results demonstrate that EdgePush significantly outperforms state-of-the-art baselines in terms of query efficiency on large motif-based and real-world weighted graphs.

Active learning is an effective approach for tasks with limited labeled data. It samples a small set of data to annotate actively and is widely applied in various AI tasks. It uses an iterative process, during which we utilize the current trained model to evaluate all unlabeled samples and annotate the best samples based on a specific query strategy to update the underlying model iteratively. Most existing active learning approaches rely on only the evaluation results generated by the current model and ignore the results from previous iterations. In this paper, we propose using more historical evaluation results which can provide additional information to help better select samples. First, we apply two kinds of heuristic features of the historical evaluation results, the weighted sum of historical results and the fluctuation of the historical evaluation sequence, to improve the effectiveness of active learning sampling. Next, to further and more globally use the information contained in the historical results, we design a novel query strategy that learns how to select samples based on the historical sequences automatically. Our proposed idea is general and can be combined with both basic and state-of-the-art query strategies to achieve improvements. We test our approaches on two common NLP tasks including text classification and named entity recognition. Experimental results show that our methods significantly promote existing methods.

A persistent data structure , also known as a multiversion data structure in the database literature, is a data structure that preserves all its previous versions as it is updated over time. Every update (inserting, deleting, or changing a data record) to the data structure creates a new version, while all the versions are kept in the data structure so that any previous version can still be queried. Persistent data structures aim at recording all versions accurately, which results in a space requirement that is at least linear to the number of updates. In many of today’s big data applications, in particular, for high-speed streaming data, the volume and velocity of the data are so high that we cannot afford to store everything. Therefore, streaming algorithms have received a lot of attention in the research community, which uses only sublinear space by sacrificing slightly on accuracy. All streaming algorithms work by maintaining a small data structure in memory, which is usually called a sketch , summary , or synopsis . The summary is updated upon the arrival of every element in the stream, thus it is ephemeral , meaning that it can only answer queries about the current status of the stream. In this article, we aim at designing persistent summaries, thereby giving streaming algorithms the ability to answer queries about the stream at any prior time.

Citation count prediction is an important task for estimating the future impact of research papers. Most of the existing works utilize the information extracted from the paper itself. In this article, we focus on how to utilize another kind of useful data signal (i.e., peer review text) to improve both the performance and interpretability of the prediction models. Specially, we propose a novel aspect-aware capsule network for citation count prediction based on review text. It contains two major capsule layers, namely the feature capsule layer and the aspect capsule layer, with two different routing approaches, respectively. Feature capsules encode the local semantics from review sentences as the input of aspect capsule layer, whereas aspect capsules aim to capture high-level semantic features that will be served as final representations for prediction. Besides the predictive capacity, we also enhance the model interpretability with two strategies. First, we use the topic distribution of the review text to guide the learning of aspect capsules so that each aspect capsule can represent a specific aspect in the review. Then, we use the learned aspect capsules to generate readable text for explaining the predicted citation count. Extensive experiments on two real-world datasets have demonstrated the effectiveness of the proposed model in both performance and interpretability.

In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.

Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile from the user’s search history, and then re-rank the documents based on the personalized matching scores between the created profile and candidate documents. In this article, we attempt to solve the personalized search problem from an alternative perspective of clarifying the user’s intention of the current query. We know that there are many ambiguous words in natural language such as “Apple.” People with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, we propose a personalized search model with personal word embeddings for each individual user that mainly contain the word meanings that the user already knows and can reflect the user interests. To learn great personal word embeddings, we design a pre-training model that captures both the textual information of the query log and the information about user interests contained in the click-through data represented as a graph structure. With personal word embeddings, we obtain the personalized word and context-aware representations of the query and documents. Furthermore, we also employ the current session as the short-term search context to dynamically disambiguate the current query. Finally, we use a matching model to calculate the matching score between the personalized query and document representations for ranking. Experimental results on two large-scale query logs show that our designed model significantly outperforms state-of-the-art personalization models.

Within online platforms, it is critical to capture the semantics of sequential user behaviors for accurately modeling user interests. However, dynamic characteristics and sparse behaviors make it difficult to train effective user representations for sequential user behavior modeling.

Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especially in the ad-hoc retrieval area. Existing pre-training approaches tailored for IR tried to incorporate weak supervised signals, such as query-likelihood based sampling, to construct pseudo query-document pairs from the raw textual corpus. However, these signals rely heavily on the sampling method. For example, the query likelihood model may lead to much noise in the constructed pre-training data. In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval. Since the anchor texts are created by webmasters and can usually summarize the target document, it can help to build more accurate and reliable pre-training samples than a specific algorithm. Considering different views of the downstream ad-hoc retrieval, we devise four pre-training tasks based on the hyperlinks. We then pre-train the Transformer model to predict the pair-wise preference, jointly with the Masked Language Model objective. Experimental results on two large-scale ad-hoc retrieval datasets show the significant improvement of our model compared with the existing methods.

In this paper, we explore the problem of developing personalized chatbots. A personalized chatbot is designed as a digital chatting assistant for a user. The key characteristic of a personalized chatbot is that it should have a consistent personality with the corresponding user. It can talk the same way as the user when it is delegated to respond to others' messages. Many methods have been proposed to assign a personality to dialogue chatbots, but most of them utilize explicit user profiles, including several persona descriptions or key-value-based personal information. In a practical scenario, however, users might be reluctant to write detailed persona descriptions, and obtaining a large number of explicit user profiles requires tremendous manual labour. To tackle the problem, we present a retrieval-based personalized chatbot model, namely IMPChat, to learn an implicit user profile from the user's dialogue history. We argue that the implicit user profile is superior to the explicit user profile regarding accessibility and flexibility. IMPChat aims to learn an implicit user profile through modeling user's personalized language style and personalized preferences separately. To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user. The personalized preferences are dynamic and context-aware: we assign higher weights to those historical pairs that are topically related to the current query when aggregating the personalized preferences. We match each response candidate with the personalized language style and personalized preference, respectively, and fuse the two matching signals to determine the final ranking score. We conduct comprehensive experiments on two large datasets, and the results show that our method outperforms all baseline models.

Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and recommendation services, showing us the demand and opportunity to simultaneously handle both tasks. However, most platforms consider these two tasks independently -- they tend to train separate search model and recommendation model, without exploiting the relatedness and dependency between them. In this paper, we argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We investigate the interactions between these two tasks in the specific information content service domain. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks based on the unified sequence. More specifically, we design the Unified Information SEarch and Recommendation model (USER), which mines user interests from the integrated sequence and accomplish the two tasks in a unified way. Experiments on a dataset from a real-world information content service platform verify that our model outperforms separate search and recommendation baselines.

In recent years, there are a large number of recommendation algorithms proposed in the literature, from traditional collaborative filtering to deep learning algorithms. However, the concerns about how to standardize open source implementation of recommendation algorithms continually increase in the research community. In the light of this challenge, we propose a unified, comprehensive and efficient recommender system library called RecBole (pronounced as [rEk'[email protected]]), which provides a unified framework to develop and reproduce recommendation algorithms for research purpose. In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation. We implement the RecBole library based on PyTorch, which is one of the most popular deep learning frameworks. Our library is featured in many aspects, including general and extensible data structures, comprehensive benchmark models and datasets, efficient GPU-accelerated execution, and extensive and standard evaluation protocols. We provide a series of auxiliary functions, tools, and scripts to facilitate the use of this library, such as automatic parameter tuning and break-point resume. Such a framework is useful to standardize the implementation and evaluation of recommender systems. The project and documents are released at https://recbole.io/.

Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made great progress in extracting personal signals from the query log and learning user representations. However, neural personalized search is extremely dependent on sufficient data to train the user model. Data sparsity is an inevitable challenge for existing methods to learn high-quality user representations. Moreover, the overemphasis on final ranking quality leads to rough data representations and impairs the generalizability of the model. To tackle these issues, we propose a Personalized Search framework with Self-supervised Learning (PSSL) to enhance data representations. Specifically, we adopt a contrastive sampling method to extract paired self-supervised information from sequences of user behaviors in query logs. Four auxiliary tasks are designed to pre-train the sentence encoder and the sequence encoder used in the ranking model. They are optimized by contrastive loss which aims to close the distance between similar user sequences, queries, and documents. Experimental results on two datasets demonstrate that our proposed model PSSL achieves state-of-the-art performance compared with existing baselines.

As it becomes prevalent that user information exists in multiple platforms or services, cross-domain recommendation has been an important task in industry. Although it is well known that users tend to show different preferences in different domains, existing studies seldom model how domain biases affect user preferences. Focused on this issue, we develop a casual-based approach to mitigating the domain biases when transferring the user information cross domains. To be specific, this paper presents a novel debiasing learning based cross-domain recommendation framework with causal embedding. In this framework, we design a novel Inverse-Propensity-Score (IPS) estimator designed for cross-domain scenario, and further propose three kinds of restrictions for propensity score learning. Our framework can be generally applied to various recommendation algorithms for cross-domain recommendation. Extensive experiments on both public and industry datasets have demonstrated the effectiveness of the proposed framework.

Efficient computation of node proximity queries such as transition probabilities, Personalized PageRank, and Katz are of fundamental importance in various graph mining and learning tasks. In particular, several recent works leverage fast node proximity computation to improve the scalability of Graph Neural Networks (GNN). However, prior studies on proximity computation and GNN feature propagation are on a case-by-case basis, with each paper focusing on a particular proximity measure.

Self-attention networks (SANs) have been intensively applied for sequential recommenders, but they are limited due to: (1) the quadratic complexity and vulnerability to over-parameterization in self-attention; (2) inaccurate modeling of sequential relations between items due to the implicit position encoding. In this work, we propose the low-rank decomposed self-attention networks (LightSANs) to overcome these problems. Particularly, we introduce the low-rank decomposed self-attention, which projects user's historical items into a small constant number of latent interests and leverages item-to-interest interaction to generate the context-aware representation. It scales linearly w.r.t. the user's historical sequence length in terms of time and space, and is more resilient to over-parameterization. Besides, we design the decoupled position encoding, which models the sequential relations between items more precisely. Extensive experimental studies are carried out on three real-world datasets, where LightSANs outperform the existing SANs-based recommenders in terms of both effectiveness and efficiency.

As a natural language generation task, it is challenging to generate informative and coherent review text. In order to enhance the informativeness of the generated text, existing solutions typically learn to copy entities or triples from knowledge graphs (KGs). However, they lack overall consideration to select and arrange the incorporated knowledge, which tends to cause text incoherence. To address the above issue, we focus on improving entity-centric coherence of the generated reviews by leveraging the semantic structure of KGs. In this paper, we propose a novel Coherence Enhanced Text Planning model (CETP) based on knowledge graphs (KGs) to improve both global and local coherence for review generation. The proposed model learns a two-level text plan for generating a document: (1) the document plan is modeled as a sequence of sentence plans in order, and (2) the sentence plan is modeled as an entity-based subgraph from KG. Local coherence can be naturally enforced by KG subgraphs through intra-sentence correlations between entities. For global coherence, we design a hierarchical self-attentive architecture with both subgraph- and node-level attention to enhance the correlations between subgraphs. To our knowledge, we are the first to utilize a KG-based text planning model to enhance text coherence for review generation. Extensive experiments on three datasets confirm the effectiveness of our model on improving the content coherence of generated texts.

Personalized chatbots focus on endowing chatbots with a consistent personality to behave like real users, give more informative responses, and further act as personal assistants. Existing personalized approaches tried to incorporate several text descriptions as explicit user profiles. However, the acquisition of such explicit profiles is expensive and time-consuming, thus being impractical for large-scale real-world applications. Moreover, the restricted predefined profile neglects the language behavior of a real user and cannot be automatically updated together with the change of user interests. In this paper, we propose to learn implicit user profiles automatically from large-scale user dialogue history for building personalized chatbots. Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses. To highlight the relevant historical responses to the input post, we further establish a key-value memory network of historical post-response pairs, and build a dynamic post-aware user profile. The dynamic profile mainly describes what and how the user has responded to similar posts in history. To explicitly utilize users' frequently used words, we design a personalized decoder to fuse two decoding strategies, including generating a word from the generic vocabulary and copying one word from the user's personalized vocabulary. Experiments on two real-world datasets show the significant improvement of our model compared with existing methods.

atural language dialogue systems raise great attention recently. As many dialogue models are data-driven, high-quality datasets are essential to these systems. In this paper, we introduce Pchatbot, a large-scale dialogue dataset that contains two subsets collected from Weibo and Judicial forums respectively. To adapt the raw dataset to dialogue systems, we elaborately normalize the raw dataset via processes such as anonymization, deduplication, segmentation, and filtering. The scale of Pchatbot is significantly larger than existing Chinese datasets, which might benefit the data-driven models. Besides, current dialogue datasets for personalized chatbot usually contain several persona sentences or attributes. Different from existing datasets, Pchatbot provides anonymized user IDs and timestamps for both posts and responses. This enables the development of personalized dialogue models that directly learn implicit user personality from the user's dialogue history. Our preliminary experimental study benchmarks several state-of-the-art dialogue models to provide a comparison for future work. The dataset can be publicly accessed at Github: https://github.com/qhjqhj00/Pchatbot.

Search result diversification aims to offer diverse documents that cover as many intents as possible. Most existing implicit diversification approaches model diversity through the similarity of document representation, which is indirect and unnatural. To handle the diversity more precisely, we measure the similarity of documents by their similarity of the intent coverage. Specifically, we build a classifier to judge whether two different documents contain the same intent based on the document's content. Then we construct an intent graph to present the complicated relationship of documents and the query. On the intent graph, documents are connected if they are similar, while the query and the document are gradually connected based on the document selection result. Then we employ graph convolutional networks (GCNs) to update the representation of the query and each document by aggregating its neighbors. By this means, we can obtain the context-aware query representation and the intent-aware document representations through the dynamic intent graph during the document selection process. Furthermore, these representations and intent graph features are fused into diversity features. Combined with the traditional relevance features, we obtain the final ranking score that balances the relevance and the diversity. Experimental results show that this implicit diversification model significantly outperforms all existing implicit diversification methods, and it can even beat the state-of-the-art explicit models.

Sequential recommendation aims at predicting users' preferences based on their historical behaviors. However, this recommendation strategy may not perform well in practice due to the sparsity of the real-world data. In this paper, we propose a novel counterfactual data augmentation framework to mitigate the impact of the imperfect training data and empower sequential recommendation models. Our framework is composed of a sampler model and an anchor model. The sampler model aims to generate new user behavior sequences based on the observed ones, while the anchor model is leveraged to provide the final recommendation list, which is trained based on both observed and generated sequences. We design the sampler model to answer the key counterfactual question: "what would a user like to buy if her previously purchased items had been different?". Beyond heuristic intervention methods, we leverage two learning-based methods to implement the sampler model, and thus, improve the quality of the generated sequences when training the anchor model. Additionally, we analyze the influence of the generated sequences on the anchor model in theory and achieve a trade-off between the information and the noise introduced by the generated sequences. Experiments on nine real-world datasets demonstrate our framework's effectiveness and generality.

The user feedbacks could be delayed in many streaming recommendation scenarios. As an example, the user feedbacks to a recommended coupon consist of the immediate feedback on the click event and the delayed feedback on the resultant conversion. The delayed feedbacks pose a challenge of training recommendation models using instances with incomplete labels. When being applied to real products, the challenge becomes more severe as the streaming recommendation models need to be retrained very frequently and the training instances need to be collected over very short time scales. Existing approaches either simply ignore the unobserved feedbacks or heuristically adjust the feedbacks on a static instance set, resulting in biases in the training data and hurting the accuracy of the learned recommenders. In this paper, we propose a novel and theoretic sound counterfactual approach to adjusting the user feedbacks and learning the recommendation models, called CBDF (Counterfactual Bandit with Delayed Feedback). CBDF formulates the streaming recommendation with delayed feedback as a problem of sequential decision making and models it with a batched bandit. To deal with the issue of delayed feedback, at each iteration (episode), a counterfactual importance sampling model is employed to re-weight the original feedbacks and generate the modified rewards. Based on the modified rewards, a batched bandit is learned for conducting online recommendation at the next iteration. Theoretical analysis showed that the modified rewards are statistically unbiased, and the learned bandit policy enjoys a sub-linear regret bound. Experimental results demonstrated that CBDF can outperform the state-of-the-art baselines on a synthetic dataset, the Criteo dataset, and a dataset from Tencent's WeChat app.

The key to personalized search is to build the user profile based on historical behaviour. To deal with the users who lack historical data, group based personalized models were proposed to incorporate the profiles of similar users when re-ranking the results. However, similar users are mostly found based on simple lexical or topical similarity in search behaviours. In this paper, we propose a neural network enhanced method to highlight similar users in semantic space. Furthermore, we argue that the behaviour-based similar users are still insufficient to understand a new query when user's historical activities are limited. To tackle this issue, we introduce the friend network into personalized search to determine the closeness between users in another way. Since the friendship is often formed based on similar background or interest, there are plenty of personalized signals hidden in the friend network naturally. Specifically, we propose a friend network enhanced personalized search model, which groups the user into multiple friend circles based on search behaviours and friend relations respectively. These two types of friend circles are complementary to construct a more comprehensive group profile for refining the personalization. Experimental results show the significant improvement of our model over existing personalized search models.

Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answer entities that are multiple hops away in the Knowl- edge Base (KB) from the entities in the question. A major challenge is the lack of supervision signals at intermediate steps. Therefore, multi-hop KBQA algorithms can only receive the feedback from the final answer, which makes the learning unstable or ineffective. To address this challenge, we propose a novel teacher-student approach for the multi-hop KBQA task. In our approach, the stu- dent network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network. The major novelty lies in the design of the teacher network, where we utilize both forward and backward reasoning to enhance the learning of intermediate entity distributions. By considering bidi- rectional reasoning, the teacher network can produce more reliable intermediate supervision signals, which can alleviate the issue of spurious reasoning. Extensive experiments on three benchmark datasets have demonstrated the effectiveness of our approach on the KBQA task.

This paper concerns user preference estimation in multi-round conversational recommender systems (CRS), which interacts with users by asking questions about attributes and recommending items multiple times in one conversation. Multi-round CRS such as EAR have been proposed in which the user's online feedback at both attribute level and item level can be utilized to estimate user preference and make recommendations. Though preliminary success has been shown, existing user preference models in CRS usually use the online feedback information as independent features or training instances, overlooking the relation between attribute-level and item-level feedback signals. The relation can be used to more precisely identify the reasons (e.g., some certain attributes) that trigger the rejection of an item, leading to more fine-grained utilization of the feedback information. To address aforementioned issue, this paper proposes a novel preference estimation model tailored for multi-round CRS, called Feedback-guided Preference Adaptation Network (FPAN). In FPAN, two gating modules are designed to respectively adapt the original user embedding and item-level feedback, both according to the online attribute-level feedback. The gating modules utilize the fine-grained attribute-level feedback to revise the user embedding and coarse-grained item-level feedback, achieving more accurate user preference estimation by considering the relation between feedback. Experimental results on two benchmarks showed that FPAN outperformed the state-of-the-art user preference models in CRS, and the multi-round CRS can also be enhanced by using FPAN as its recommender component.

The evolution of mobile apps has greatly changed the way that we live. It becomes increasingly important to understand and model the users on mobile apps. Instead of focusing on some specific app alone, it has become a popular paradigm to study the user behavior on various mobile apps in a symbiotic environment.

Personalized search returns each user more accurate results by collecting the user’s historical search behaviors to infer her interests and query intents. However, it brings the risk of user privacy leakage, and this may greatly limit the practical application of personalized search. In this paper, we focus on the problem of privacy protection in personalized search, and propose a privacy protection enhanced personalized search framework, denoted with FedPS. Under this framework, we keep each user’s private data on her individual client, and train a shared personalized ranking model with all users’ decentralized data by means of federated learning. We implement two models within the framework: the first one applies a personalization model with a personal module that fits the user’s data distribution to alleviate the challenge of data heterogeneity in federated learning; the second model introduces trustworthy proxies and group servers to solve the problems of limited communication, performance bottleneck and privacy attack for FedPS. Experimental results verify that our proposed framework can enhance privacy protection without losing too much accuracy.

Conversational systems now attract great attention due to their promising potential and commercial values. To build a conversational system with moderate intelligence is challenging and requires big (conversational) data, as well as interdisciplinary techniques. Thanks to the prosperity of the Web, the massive data available greatly facilitate data-driven methods such as deep learning for human-computer conversational systems. In general, retrieval-based conversational systems apply various matching schema between query utterances and responses, but the classic retrieval paradigm suffers from prominent weakness for conversations: the system finds similar responses given a particular query. For real human-to-human conversations, on the contrary, responses can be greatly different yet all are possibly appropriate. The observation reveals the diversity phenomenon in conversations. In this article, we ascribe the lack of conversational diversity to the reason that the query utterances are statically modeled regardless of candidate responses through traditional methods. To this end, we propose a dynamic representation learning strategy that models the query utterances and different response candidates in an interactive way. To be more specific, we propose a Respond-with-Diversity model augmented by the memory module interacting with both the query utterances and multiple candidate responses. Hence, we obtain dynamic representations for the input queries conditioned on different response candidates. We frame the model as an end-to-end learnable neural network. In the experiments, we demonstrate the effectiveness of the proposed model by achieving a good appropriateness score and much better diversity in retrieval-based conversations between humans and computers.

Personalized search is a promising way to improve search qualities by taking user interests into consideration. Recently, machine learning and deep learning techniques have been successfully applied to search result personalization. Most existing models simply regard the personal search history as a static set of user behaviors and learn fixed ranking strategies based on all the recorded data. Though improvements have been achieved, the essence that the search process is a sequence of interactions between the search engine and user is ignored. The user’s interests may dynamically change during the search process, therefore, it would be more helpful if a personalized search model could track the whole interaction process and adjust its ranking strategy continuously. In this article, we adapt reinforcement learning to personalized search and propose a framework, referred to as RLPS. It utilizes a Markov Decision Process ( MDP ) to track sequential interactions between the user and search engine, and continuously update the underlying personalized ranking model with the user’s real-time feedback to learn the user’s dynamic interests. Within this framework, we implement two models: the listwise RLPS-L and the hierarchical RLPS-H. RLPS-L interacts with users and trains the ranking model with document lists, while RLPS-H improves model training by designing a layered structure and introducing document pairs. In addition, we also design a feedback-aware personalized ranking component to capture the user’s feedback, which impacts the user interest profile for the next query. Significant improvements over existing personalized search models are observed in the experiments on the public AOL search log and a commercial log.

With the ever-increasing growth of online recruitment data, job-resume matching has become an important task to automatically match jobs with suitable resumes. This task is typically casted as a supervised text matching problem. Supervised learning is powerful when the labeled data is sufficient. However, on online recruitment platforms, job-resume interaction data is sparse and noisy, which affects the performance of job-resume match algorithms.

Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledgeenhanced PRG model based on capsule graph neural network (CapsGNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task.

Personalized search aims to improve the search quality by re-ranking the candidate document list based on user's historical behavior. Existing approaches focus on modeling the order information of user's search history by sequential methods such as Recurrent Neural Network (RNN). However, these methods usually ignore the fine-grained time information associated with user actions. In fact, the time intervals between queries can help to capture the evolution of query intent and document interest of users. Besides, the time intervals between past actions and current query can reflect the re-finding tendency more accurately than discrete steps in RNN. In this paper, we propose PSTIE, a fine-grained Time Information Enhanced model to construct more accurate user interest representations for Personalized Search. To capture the short-term interest of users, we design time-aware LSTM architectures for modeling the subtle interest evolution of users in continuous time. We further leverage time in calculating the re-finding possibility of users to capture the long-term user interest. We propose two methods to utilize the time-enhanced user interest into personalized ranking. Experiments on two datasets show that PSTIE can effectively improve the ranking quality over state-of-the-art models.

Search results returned by search engines need to be diversified in order to satisfy different information needs of different users. Several supervised learning models have been proposed for diversifying search results in recent years. Most of the existing supervised methods greedily compare each candidate document with the selected document sequence and select the next local optimal document. However, the information utility of each candidate document is not independent with each other, and research has shown that the selection of a candidate document will affect the utilities of other candidate documents. As a result, the local optimal document rankings will not lead to the global optimal rankings. In this paper, we propose a new supervised diversification framework to address this issue. Based on a self-attention encoder-decoder structure, the model can take the whole candidate document sequence as input, and simultaneously leverage both the novelty and the subtopic coverage of the candidate documents. We call this framework Diversity Encoder with Self-Attention (DESA). Comparing with existing supervised methods, this framework can model the interactions between all candidate documents and return their diversification scores based on the whole candidate document sequence. Experimental results show that our proposed framework outperforms existing methods. These results confirm the effectiveness of modeling all the candidate documents for the overall novelty and subtopic coverage globally, instead of comparing every single candidate document with the selected sequence document selection.

Top-N item recommendation has been a widely studied task from implicit feedback. Although much progress has been made with neural methods, there is increasing concern on appropriate evaluation of recommendation algorithms. In this paper, we revisit alternative experimental settings for evaluating top-N recommendation algorithms, considering three important factors, namely dataset splitting, sampled metrics and domain selection. We select eight representative recommendation algorithms (covering both traditional and neural methods) and construct extensive experiments on a very large dataset. By carefully revisiting different options, we make several important findings on the three factors, which directly provide useful suggestions on how to appropriately set up the experiments for top-N item recommendation.

Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations. However, the model trained with this loss is prone to suffer from data sparsity problem. Since it overemphasizes the final performance, the association or fusion between context data and sequence data has not been well captured and utilized for sequential recommendation.

Recently, conversational recommender system (CRS) has become an emerging and practical research topic. Most of the existing CRS methods focus on learning effective preference representations for users from conversation data alone. While, we take a new perspective to leverage historical interaction data for improving CRS. For this purpose, we propose a novel pre-training approach to integrating both item-based preference sequence (from historical interaction data) and attribute-based preference sequence (from conversation data) via pre-training methods. We carefully design two pre-training tasks to enhance information fusion between item- and attribute-based preference. To improve the learning performance, we further develop an effective negative sample generator which can produce high-quality negative samples. Experiment results on two real-world datasets have demonstrated the effectiveness of our approach for improving CRS.

Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major issues still remain to be solved. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference.

Most commercial industrial recommender systems have built their closed feedback loops. Though it is helpful in item recommendation and model training, the closed feedback loop may lead to the so-called bias problems, including the position bias, selection bias and popularity bias. The recommendation models trained with biased may hurt the user experiences by recommending homogenous items. How to control the biases in the closed feedback loop has become one of major challenges in modern recommender systems. This talk discusses the counterfactual learning technologies for tackling the bias problem in recommendation.

Search result diversification aims to retrieve diverse results to cover as many subtopics related to the query as possible. Recent studies showed that supervised diversification models are able to outperform the heuristic approaches, by automatically learning a diversification function other than using manually designed score functions. The main challenge of training a diversification model is the lack of high-quality training samples. Due to the involvement of dependence between documents in the ranker, it is very hard for training algorithms to select effective positive and negative ranking lists to train a reliable ranking model, given a large number of candidate documents within which different documents are relevant to different subtopics. To tackle this problem, we propose a supervised diversification framework based on Generative Adversarial Network (GAN). It consists of a generator and a discriminator interacting with each other in a minimax game. Specifically, the generator generates more confusing negative samples for the discriminator, and the discriminator sends back complementary ranking signals to the generator. Furthermore, we explicitly exploit subtopics in the generator, whereas focusing on modeling document similarity in the discriminator. Through such a minimax game, we are able to obtain better ranking models by combining ranking signals learned by the generator and the discriminator. Experimental results on the TREC Web Track dataset show that the proposed method can significantly outperform existing diversification methods.

This paper presents a knowledge graph enhanced personalized search model, KEPS. For each user and her queries, KEPS first con- ducts personalized entity linking on the queries and forms better intent representations; then it builds a knowledge enhanced profile for the user, using memory networks to store the predicted search intents and linked entities in her search history. The knowledge enhanced user profile and intent representation are then utilized by KEPS for better, knowledge enhanced, personalized search. Furthermore, after providing personalized search for each query, KEPS leverages user's feedback (click on documents) to post-adjust the entity linking on previous queries. This fixes previous linking errors and improves ranking quality for future queries. Experiments on the public AOL search log demonstrate the advantage of knowledge in personalized search: personalized entity linking better reflects user's search intent, the memory networks better maintain user's subtle preferences, and the post linking adjustment fixes some linking errors with the received feedback signals. The three components together lead to a significantly better ranking accuracy of KEPS.

In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal ranking model would be a mapping from a document set to a permutation on the set, and should satisfy two critical requirements: (1) it should have the ability to model cross-document interactions so as to capture local context information in a query; (2) it should be permutation-invariant, which means that any permutation of the inputted documents would not change the output ranking. Previous studies on learning-to-rank either design uni-variate scoring functions that score each document separately, and thus failed to model the cross-document interactions; or construct multivariate scoring functions that score documents sequentially, which inevitably sacrifice the permutation invariance requirement. In this paper, we propose a neural learning-to-rank model called SetRank which directly learns a permutation-invariant ranking model defined on document sets of any size. SetRank employs a stack of (induced) multi-head self attention blocks as its key component for learning the embeddings for all of the retrieved documents jointly. The self-attention mechanism not only helps SetRank to capture the local context information from cross-document interactions, but also to learn permutation-equivariant representations for the inputted documents, which therefore achieving a permutation-invariant ranking model. Experimental results on three benchmarks showed that the SetRank significantly outperformed the baselines include the traditional learning-to-rank models and state-of-the-art Neural IR models.

Recently, deep learning has made significant progress in the task of sequential recommendation. Existing neural sequential recommenders typically adopt a generative way trained with Maximum Likelihood Estimation (MLE). When context information (called factor) is involved, it is difficult to analyze when and how each individual factor would affect the final recommendation performance.

This paper concerns reinforcement learning~(RL) of the document ranking models for information retrieval~(IR). One branch of the RL approaches to ranking formalize the process of ranking with Markov decision process~(MDP) and determine the model parameters with policy gradient. Though preliminary success has been shown, these approaches are still far from achieving their full potentials. Existing policy gradient methods directly utilize the absolute performance scores (returns) of the sampled document lists in its gradient estimations, which may cause two limitations: 1) fail to reflect the relative goodness of documents within the same query, which usually is close to the nature of IR ranking; 2) generate high variance gradient estimations, resulting in slow learning speed and low ranking accuracy. To deal with the issues, we propose a novel policy gradient algorithm in which the gradients are determined using pairwise comparisons of two document lists sampled within the same query. The algorithm, referred to as Pairwise Policy Gradient (PPG), repeatedly samples pairs of document lists, estimates the gradients with pairwise comparisons, and finally updates the model parameters. Theoretical analysis shows that PPG makes an unbiased and low variance gradient estimations. Experimental results have demonstrated performance gains over the state-of-the-art baselines in search result diversification and text retrieval.

Personalized search is a task to tailor the general document ranking list based on user interests to better satisfy the user's information need. Many personalized search models have been proposed and demonstrated their capability to improve search quality. The general idea of most approaches is to build a user interest profile according to the user's search history, and then re-rank the documents based on the matching scores between the created user profile and candidate documents. In this paper, we propose to solve the problem of personalized search in an alternative way. We know that there are many ambiguous words in natural language such as 'Apple', and people with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, for different users, such a word should own different semantic representations. Motivated by this idea, we design a personalized search model based on personal word embeddings, referred to as PEPS. Specifically, we train personal word embeddings for each user in which the representation of each word is mainly decided by the user's personal data. Then, we obtain the personalized word and contextual representations of the query and documents with an attention function. Finally, we use a matching model to calculate the matching score between the personalized query and document representations. Experiments on two datasets verify that our model can significantly improve state-of-the-art personalization models.

The key to personalized search is to clarify the meaning of current query based on user's search history. Previous personalized studies tried to build user profiles on the basis of historical data to tailor the ranking. However, we argue that the user profile based methods do not really disambiguate the current query. They still retain some semantic bias when building user profiles. In this paper, we propose to encode history with context-aware representation learning to enhance the representation of current query, which is a direct way to clarify the user's information need. Specifically, endowed with the benefit from transformer on aggregating contextual information, we devise a query disambiguation model to parse the meaning of current query in multiple stages. Moreover, for covering the cases that current query is not sufficient to express the intent, we train a personalized language model to predict user intent from existing queries. Under the interaction of two sub-models, we can generate the context-aware representation of current query and re-rank the results based on it. Experimental results show the significant improvement of our model compared with previous methods.

SimRank is a popular measurement for evaluating the node-to-node similarities based on the graph topology. In recent years, single-source and top-k SimRank queries have received increasing attention due to their applications in web mining, social network analysis, and spam detection. However, a fundamental obstacle in studying SimRank has been the lack of ground truths. The only exact algorithm, Power Method, is computationally infeasible on graphs with more than 106 nodes. Consequently, no existing work has evaluated the actual trade-offs between query time and accuracy on large real-world graphs. In this paper, we present ExSim, the first algorithm that computes the exact single-source and top-k SimRank results on large graphs. With high probability, this algorithm produces ground truths with a rigorous theoretical guarantee. We conduct extensive experiments on real-world datasets to demonstrate the efficiency of ExactSim. The results show that ExactSim provides the ground truth for any single-source SimRank query with a precision up to 7 decimal places within a reasonable query time.

The goal of personalized search is to tailor the document ranking list to meet user's individual needs. Previous studies showed users usually look for the information that has been searched before. This is called re-finding behavior which is widely explored in existing personalized search approaches. However, most existing methods for identifying re-finding behavior focus on simple lexical similarities between queries. In this paper, we propose to construct memory networks (MN) to support the identification of more complex re-finding behavior. Specifically, incorporating semantic information, we devise two external memories to make an expansion of re-finding based on the query and the document respectively. We further design an intent memory to recognize session-based re-finding behavior. Endowed with these memory networks, we can build a fine-grained user model dynamically based on the current query and documents, and use the model to re-rank the results. Experimental results show the significant improvement of our model compared with traditional methods.

The task of Knowledge Graph Completion (KGC) aims to automatically infer the missing fact information in Knowledge Graph (KG). In this paper, we take a new perspective that aims to leverage rich user-item interaction data (user interaction data for short) for improving the KGC task. Our work is inspired by the observation that many KG entities correspond to online items in application systems. However, the two kinds of data sources have very different intrinsic characteristics, and it is likely to hurt the original performance using simple fusion strategy.

Personalized search improves generic ranking models by taking user interests into consideration and returning more accurate search results to individual users. In recent years, machine learning and deep learning techniques have been successfully applied in personalized search. Most existing personalization models simply regard the search history as a static set of user behaviours and learn fixed ranking strategies based on the recorded data. Though improvements have been observed, it is obvious that these methods ignore the dynamic nature of the search process: search is a sequence of interactions between the search engine and the user. During the search process, the user interests may dynamically change. It would be more helpful if a personalized search model could track the whole interaction process and update its ranking strategy continuously. In this paper, we propose a reinforcement learning based personalization model, referred to as RLPer, to track the sequential interactions between the users and search engine with a hierarchical Markov Decision Process (MDP). In RLPer, the search engine interacts with the user to update the underlying ranking model continuously with real-time feedback. And we design a feedback-aware personalized ranking component to catch the user’s feedback which has impacts on the user interest profile for the next query. Experimental results on the publicly available AOL search log verify that our proposed model can significantly outperform state-of-the-art personalized search models.

This article presents a network embedding approach to automatically generate tags for microblog users. Instead of using text data, we aim to annotate microblog users with meaningful tags by leveraging rich social link data. To utilize directed social links, we use two kinds of node representations for modeling user interest in terms of their followers and followees, respectively. To alleviate the sparsity problem, we propose a novel method based on two transformation functions for capturing implicit interest similarity. Different from previous works on capturing high-order proximity, our model is able to directly characterize the effect of the context user on the proximity of node pairs. Another novelty of our model is that the importance scores of users learned from the classic PageRank algorithm are utilized to set the link weights. By using such weights, our model is more capable of disentangling the interest similarity evidence of a link. We jointly consider the above factors when designing the final objective function. We construct a very large evaluation set consisting of 2.6M users, 0.5M tags, and 0.8B following links. To our knowledge, it is the largest reported dataset for microblog user tagging in the literature. Extensive experiments on this dataset demonstrate the effectiveness of the proposed approach. We implement this approach with several optimization techniques, which makes our model easy to scale to very large social networks. Ubiquitous social links provide important data resources to understand user interests. Our work provides an effective and efficient solution to annotate user interests solely using the link data, which has important practical value in industry. To illustrate the use of our models, we implement a demonstration system for visualizing, navigating, and searching microblog users.

We bring the novel idea of exploiting motifs into network embedding, in a dual-level network representation learning model called RUM (network Representation learning Using Motifs). Towards the leveraging of graph motifs that constitute higher-order organizations in a network, we propose two strategies, namely MotifWalk and MotifRe-weighting for learning motif-aware network embeddings. Motif-based and node-based representations are simultaneously generated, so that both the high-order structures and each node's individual properties are preserved in the final embeddings. We demonstrate that RUM has strong and well-balanced capability of preserving lowerorder proximities while discovering and capturing higher-order network structures. In empirical evaluation, RUM is tested on multiple public datasets, that range from small to medium citation networks to a large social network with more than a million nodes. Results show that the use of motifs in the representation learning process brings substantial benefits in reallife tasks, resulting in up to 12% microF1 and 8% macroF1 relative gains for node classification performance over the bestperforming competing methods.

In e-commerce, users' demands are not only conditioned by their profile and preferences, but also by their recent purchases that may generate new demands, as well as periodical demands that depend on purchases made some time ago. We call them respectively short-term demands and long-term demands. In this paper, we propose a novel self-attentive Continuous-Time Recommendation model (CTRec) for capturing the evolving demands of users over time. For modeling such time-sensitive demands, a Demand-aware Hawkes Process (DHP) framework is designed in CTRec to learn from the discrete purchase records of users. More specifically, a convolutional neural network is utilized to capture the short-term demands; and a self-attention mechanism is employed to capture the periodical purchase cycles of long-term demands. All types of demands are fused in DHP to make final continuous-time recommendations. We conduct extensive experiments on four real-world commercial datasets to demonstrate that CTRec is effective for general sequential recommendation problems, including next-item and next-session/basket recommendations. We observe in particular that CTRec is capable of learning the purchase cycles of products and estimating the purchase time of a product given a user.

Fine-grained image classification and retrieval become topical in both computer vision and information retrieval. In real-life scenarios, fine-grained tasks tend to appear along with coarse-grained tasks when the observed object is coming closer. However, in previous works, the combination of fine-grained and coarse-grained tasks was often ignored. In this paper, we define a new problem called coarse-to-fine grained classification (C2FGC) which aims to recognize the classes of objects in multiple resolutions (from low to high). To solve this problem, we propose a novel Multi-linear Pooling with Hierarchy (MLPH) model. Specifically, we first design a multi-linear pooling module to include both trilinear and bilinear pooling, and then formulate the coarse-grained and fine-grained tasks within a unified framework. Experiments on two benchmark datasets show that our model achieves state-of-the-art results.

In this paper we propose a novel reinforcement learning based model for named entity recognition (NER), referred to as MM-NER. Inspired by the methodology of the AlphaGo Zero, MM-NER formalizes the problem of named entity recognition with a Monte-Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign an NER tag to a word. Two Gated Recurrent Units (GRU) are used to summarize the past tag assignments and words in the sentence. Based on the outputs of GRUs, the policy for guiding the tag assignment and the value for predicting the whole tagging accuracy of the whole sentence are produced. The policy and value are then strengthened with MCTS, which takes the produced raw policy and value as inputs, simulates and evaluates the possible tag assignments at the subsequent positions, and outputs a better search policy for assigning tags. A reinforcement learning algorithm is proposed to train the model parameters. Empirically, we show that MM-NER can accurately predict the tags thanks to the exploratory decision making mechanism introduced by MCTS. It outperformed the conventional sequence tagging baselines and performed equally well with the state-of-the-art baseline BLSTM-CRF.

Personalized search aims to adapt document ranking to user's personal interests. Traditionally, this is done by extracting click and topical features from historical data in order to construct a user profile. In recent years, deep learning has been successfully used in personalized search due to its ability of automatic feature learning. However, the small amount of noisy personal data poses challenges to deep learning models to learn the personalized classification boundary between relevant and irrelevant results. In this paper, we propose PSGAN, a Generative Adversarial Network (GAN) framework for personalized search. By means of adversarial training, we enforce the model to pay more attention to training data that are difficult to distinguish. We use the discriminator to evaluate personalized relevance of documents and use the generator to learn the distribution of relevant documents. Two alternative ways to construct the generator in the framework are tested: based on the current query or based on a set of generated queries. Experiments on data from a commercial search engine show that our models can yield significant improvements over state-of-the-art models.

SimRank is a classic measure of the similarities of nodes in a graph. Given a node u in graph $G =(V, E)$, a \em single-source SimRank query returns the SimRank similarities $s(u, v)$ between node u and each node $v \in V$. This type of queries has numerous applications in web search and social networks analysis, such as link prediction, web mining, and spam detection. Existing methods for single-source SimRank queries, however, incur query cost at least linear to the number of nodes n, which renders them inapplicable for real-time and interactive analysis. This paper proposes \prsim, an algorithm that exploits the structure of graphs to efficiently answer single-source SimRank queries. \prsim uses an index of size $O(m)$, where m is the number of edges in the graph, and guarantees a query time that depends on the \em reverse PageRank distribution of the input graph. In particular, we prove that \prsim runs in sub-linear time if the degree distribution of the input graph follows the power-law distribution, a property possessed by many real-world graphs. Based on the theoretical analysis, we show that the empirical query time of all existing SimRank algorithms also depends on the reverse PageRank distribution of the graph. Finally, we present the first experimental study that evaluates the absolute errors of various SimRank algorithms on large graphs, and we show that \prsim outperforms the state of the art in terms of query time, accuracy, index size, and scalability.

In this paper, we focus on the task of sequential recommendation using taxonomy data. Existing sequential recommendation methods usually adopt a single vectorized representation for learning the overall sequential characteristics, and have a limited modeling capacity in capturing multi-grained sequential characteristics over context information. Besides, existing methods often directly take the feature vectors derived from context information as auxiliary input, which is difficult to fully exploit the structural patterns in context information for learning preference representations. To address above issues, we propose a novel Taxonomy-aware Multi-hop Reasoning Network, named TMRN, which integrates a basic GRU-based sequential recommender with an elaborately designed memory-based multi-hop reasoning architecture. For enhancing the reasoning capacity, we incorporate taxonomy data as structural knowledge to instruct the learning of our model. We associate the learning of user preference in sequential recommendation with the category hierarchy in the taxonomy. Given a user, for each recommendation, we learn a unique preference representation corresponding to each level in the taxonomy based on her/his overall sequential preference. In this way, the overall, coarse-grained preference representation can be gradually refined in different levels from general to specific, and we are able to capture the evolvement and refinement of user preference over the taxonomy, which makes our model highly explainable. Extensive experiments show that our proposed model is superior to state-of-the-art baselines in terms of both effectiveness and interpretability.

Presents corrections to author information from the paper, “Characterizing and predicting early reviewers for effective product marketing on e-commerce websites,” (Bai, T., et al), IEEE Trans. Knowl. Data Eng., vol. 30, no. 12, pp. 2271–2284, Dec. 2018.

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.

We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in applications such as trip planning and recommendation. To process the TSR query, a set of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

Next basket recommendation is a new type of recommendation, which recommends a set of items, or a basket, to the user. Purchase in basket is a common behavior of consumers. Recently, deep neural networks have been applied to model sequential transactions of baskets in next basket recommendation. However, current methods do not track the user's evolving appetite for items explicitly, and they ignore important item attributes such as product category. In this paper, we propose a novel Attribute-aware Neural Attentive Model (ANAM) to address these problems. ANAM adopts an attention mechanism to explicitly model user's evolving appetite for items, and utilizes a hierarchical architecture to incorporate the attribute information. In specific, ANAM utilizes a recurrent neural network to model the user's sequential behavior over time, and relays the user's appetite toward items and their attributes to next basket through attention weights shared across baskets on the two different hierarchies. Experiment results on two public datasets (ıe Ta-Feng and JingDong) demonstrate the effectiveness of our ANAM model for next basket recommendation.

With the revival of neural networks, many studies try to adapt powerful sequential neural models, ıe Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based networks encode historical interaction records into a hidden state vector. Although the state vector is able to encode sequential dependency, it still has limited representation power in capturing complicated user preference. It is difficult to capture fine-grained user preference from the interaction sequence. Furthermore, the latent vector representation is usually hard to understand and explain. To address these issues, in this paper, we propose a novel knowledge enhanced sequential recommender. Our model integrates the RNN-based networks with Key-Value Memory Network (KV-MN). We further incorporate knowledge base (KB) information to enhance the semantic representation of KV-MN. RNN-based models are good at capturing sequential user preference, while knowledge-enhanced KV-MNs are good at capturing attribute-level user preference. By using a hybrid of RNNs and KV-MNs, it is expected to be endowed with both benefits from these two components. The sequential preference representation together with the attribute-level preference representation are combined as the final representation of user preference. With the incorporation of KB information, our model is also highly interpretable. To our knowledge, it is the first time that sequential recommender is integrated with external memories by leveraging large-scale KB information.

Personalized PageRank (PPR) is a classic metric that measures the relevance of graph nodes with respect to a source node. Given a graph G, a source node s, and a parameter k, a top-k PPR query returns a set of k nodes with the highest PPR values with respect to s. This type of queries serves as an important building block for numerous applications in web search and social networks, such as Twitter's Who-To-Follow recommendation service. Existing techniques for top-k PPR, however, suffer from two major deficiencies. First, they either incur prohibitive space and time overheads on large graphs, or fail to provide any guarantee on the precision of top-k results (i.e., the results returned might miss a number of actual top-k answers). Second, most of them require significant pre-computation on the input graph G, which renders them unsuitable for graphs with frequent updates (e.g., Twitter's social graph).

This article studies the problem of learning effective representations for Location-Based Social Networks (LBSN), which is useful in many tasks such as location recommendation and link prediction. Existing network embedding methods mainly focus on capturing topology patterns reflected in social connections, while check-in sequences, the most important data type in LBSNs, are not directly modeled by these models. In this article, we propose a representation learning method for LBSNs called as JRLM++, which models check-in sequences together with social connections. To capture sequential relatedness, JRLM++ characterizes two levels of sequential contexts, namely fine-grained and coarse-grained contexts. We present a learning algorithm tailored to the hierarchical architecture of the proposed model. We conduct extensive experiments on two important applications using real-world datasets. The experimental results demonstrate the superiority of our model. The proposed model can generate effective representations for both users and locations in the same embedding space, which can be further utilized to improve multiple LBSN tasks.

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namelyearly,majority, andlaggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers’ ratings and their received helpfulness scores are likely to influence product popularity. By viewing the review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines.

Search result diversification aims to retrieve diverse results to satisfy as many different information needs as possible. Supervised methods have been proposed recently to learn ranking functions and they have been shown to produce superior results to unsupervised methods. However, these methods use implicit approaches based on the principle of Maximal Marginal Relevance (MMR). In this paper, we propose a learning framework for explicit result diversification where subtopics are explicitly modeled. Based on the information contained in the sequence of selected documents, we use the attention mechanism to capture the subtopics to be focused on while selecting the next document, which naturally fits our task of document selection for diversification. As a preliminary attempt, we employ recurrent neural networks and max pooling to instantiate the framework. We use both distributed representations and traditional relevance features to model documents in the implementation. The framework is flexible to model query intent in either a flat list or a hierarchy. Experimental results show that the proposed method significantly outperforms all the existing search result diversification approaches.

Search result diversification aims at returning diversified document lists to cover different user intents of a query. Existing diversity measures assume that the intents of a query are disjoint, and do not consider their relationships. In this paper, we introduce intent hierarchies to model the relationships between intents, and present four weighing schemes. Based on intent hierarchies, we propose several hierarchical measures that take into account the relationships between intents. We demonstrate the feasibility of hierarchical measures by using a new test collection based on TREC Web Track 2009-2013 diversity test collections and by using NTCIR-11 IMine test collection. Our main experimental findings are: (1) Hierarchical measures are more discriminative and intuitive than existing measures. In terms of intuitiveness, it is preferable for hierarchical measures to use the whole intent hierarchies than to use only the leaf nodes. (2) The types of intent hierarchies used affect the discriminative power and intuitiveness of hierarchical measures. We suggest the best type of intent hierarchies to be used according to whether the nonuniform weights are available. (3) To measure the benefits of the diversification algorithms which use automatically mined hierarchical intents, it is important to use hierarchical measures instead of existing measures.

Online Judge (OJ) systems have been widely used in many areas, including programming, mathematical problems solving, and job interviews. Unlike other online learning systems, such as Massive Open Online Course, most OJ systems are designed for self-directed learning without the intervention of teachers. Also, in most OJ systems, problems are simply listed in volumes and there is no clear organization of them by topics or difficulty levels. As such, problems in the same volume are mixed in terms of topics or difficulty levels. By analyzing large-scale users’ learning traces, we observe that there are two major learning modes (or patterns). Users either practice problems in a sequential manner from the same volume regardless of their topics or they attempt problems about the same topic, which may spread across multiple volumes. Our observation is consistent with the findings in classic educational psychology. Based on our observation, we propose a novel two-mode Markov topic model to automatically detect the topics of online problems by jointly characterizing the two learning modes. For further predicting the difficulty level of online problems, we propose a competition-based expertise model using the learned topic information. Extensive experiments on three large OJ datasets have demonstrated the effectiveness of our approach in three different tasks, including skill topic extraction, expertise competition prediction and problem recommendation.

Recently, deep neural networks have been widely applied to recommender systems. A representative work is to utilize deep learning for modeling complex user-item interactions. However, similar to traditional latent factor models by factorizing user-item interactions, they tend to be ineffective to capture localized information. Localized information, such as neighborhood, is important to recommender systems in complementing the user-item interaction data. Based on this consideration, we propose a novel Neighborhood-based Neural Collaborative Filtering model (NNCF). To the best of our knowledge, it is the first time that the neighborhood information is integrated into the neural collaborative filtering methods. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model for the implicit recommendation task.

We propose and investigate a novel query, the Collective Travel Planning (CTP) query, that finds the lowest-cost route connecting multiple query sources and a destination via at most k meeting points. This type of query is useful in organizing large events, and it can bring significant benefits to society and the environment: it can help optimize the allocation of transportation resources, reduce resource consumption, and enable smarter and greener transportation; and it can help reduce greenhouse-gas emissions and traffic congestion.

Search result diversification aims to retrieve diverse results to satisfy as many different information needs as possible. Supervised methods have been proposed recently to learn ranking functions and they have been shown to produce superior results to unsupervised methods. However, these methods use implicit approaches based on the principle of Maximal Marginal Relevance (MMR). In this paper, we propose a learning framework for explicit result diversification where subtopics are explicitly modeled. Based on the information contained in the sequence of selected documents, we use attention mechanism to capture the subtopics to be focused on while selecting the next document, which naturally fits our task of document selection for diversification. The framework is implemented using recurrent neural networks and max-pooling which combine distributed representations and traditional relevance features. Our experiments show that the proposed method significantly outperforms all the existing methods.

The entity set expansion problem is to expand a small set of seed entities to a more complete set of similar entities. It can be applied in applications such as web search, item recommendation and query expansion. Traditionally, people solve this problem by exploiting the co-occurrence of entities within web pages, where latent semantic correlation among seed entities cannot be revealed. We propose a novel approach to solve the problem using knowledge graphs, by considering the deficiency (e.g., incompleteness) of knowledge graphs. We design an effective ranking model based on the semantic features of seeds to retrieve the candidate entities. Extensive experiments on public datasets show that the proposed solution significantly outperforms the state-of-the-art techniques.

A query facet is a significant list of information nuggets that explains an underlying aspect of a query. Existing algorithms mine facets of a query by extracting frequent lists contained in top search results. The coverage of facets and facet items mined by these kind of methods might be limited, because only a small number of search results are used. In order to solve this problem, we propose mining query facets by using knowledge bases which contain high-quality structured data. Specifically, we first generate facets based on the properties of the entities which are contained in Freebase and correspond to the query. Second, we mine initial query facets from search results, then expanding them by finding similar entities from Freebase. Experimental results show that our proposed method can significantly improve the coverage of facet items over the state-of-the-art algorithms.

With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

Knowledge bases have shown their effectiveness in facilitating services like Web search and question-answering. Nevertheless, it remains challenging for ordinary users to fully understand the structure of a knowledge base and to issue structural queries. In many cases, users may have a natural language question and also know some popular (but not all) entities as sample answers. In this paper, we study the Reverse top-k Neighborhood Pattern Query problem, with the aim of discovering structural queries of the question based on: (i) the structure of the knowledge base, and (ii) the sample answers of the question. The proposed solution contains two phases: filter and refine. In the filter phase, a search space of candidate queries is systematically explored. The invalid queries whose result sets do not fully cover the sample answers are filtered out. In the refine phase, all surviving queries are verified to ensure that they are sufficiently relevant to the sample answers, with the assumption that the sample answers are more well-known or popular than other entities in the results of relevant queries. Several optimization techniques are proposed to accelerate the refine phrase. For evaluation, we conduct extensive experiments using the DBpedia knowledge base and a set of real-life questions. Empirical results show that our algorithm is able to provide a small set of possible queries, which contains the query matching the user question in natural language.

The prediction of periodical time-series remains challenging due to various types of scaling, misalignments and distortion effects. Here, we propose a novel model called Temporal embedding-enhanced convolutional neural Network (TeNet) to learn repeatedly-occurring-yet-hidden structural elements in periodical time-series, called abstract snippet detectors, to predict future changes. Our model effectively learns a new feature space for a time-series dataset. In the new feature space, distorted time-series that have implicit similarity but substantial differences in value and sequence to regular patterns are re-aligned to the regular patterns in the dataset, and subsequently contribute to a robust prediction mode. The model is robust to various types of distortions and misalignments and demonstrates strong prediction power for periodical time-series. We conduct extensive experiments and discover that the proposed model shows significant and consistent advantages over existing methods on a variety of data modalities ranging from human mobility to household power consumption records, when evaluated under four metrics. The model is also robust to various factors such as number of samples, variance of data, numerical ranges of data etc. The experiments verify that the intuition behind the model can be generalized to multiple data types and applications and promises significant improvement in prediction performance across the datasets studied.

Search result diversification aims at returning diversified document lists to cover different user intents for ambiguous or broad queries. Existing diversity measures assume that user intents are independent or exclusive, and do not consider the relationships among the intents. In this paper, we introduce intent hierarchies to model the relationships among intents. Based on intent hierarchies, we propose several hierarchical measures that can consider the relationships among intents. We demonstrate the feasibility of hierarchical measures by using a new test collection based on TREC Web Track 2009-2013 diversity test collections. Our main experimental findings are: (1) Hierarchical measures are generally more discriminative and intuitive than existing measures using flat lists of intents; (2) When the queries have multilayer intent hierarchies, hierarchical measures are less correlated to existing measures, but can get more improvement in discriminative power; (3) Hierarchical measures are more intuitive in terms of diversity or relevance. The hierarchical measures using the whole intent hierarchies are more intuitive than only using the leaf nodes in terms of diversity and relevance.

Large-scale matrix computation becomes essential for many data data applications, and hence the problem of sketching matrix with small space and high precision has received extensive study for the past few years. This problem is often considered in the row-update streaming model, where the data set is a matrix A -- Rn x d, and the processor receives a row (1 x d) of A at each timestamp. The goal is to maintain a smaller matrix (termed approximation matrix, or simply approximation) B -- Rl x d as an approximation to A, such that the covariance error |AT A - BTB| is small and l ll n.

Interpreting the user intent in search queries is a key task in query understanding. Query intent classification has been widely studied. In this article, we go one step further to understand the query from the view of head--modifier analysis. For example, given the query “popular iphone 5 smart cover,” instead of using coarse-grained semantic classes (e.g.,find electronic product), we interpret that “smart cover” is the head or the intent of the query and “iphone 5” is its modifier. Query head--modifier detection can help search engines to obtain particularly relevant content, which is also important for applications such as ads matching and query recommendation. We introduce an unsupervised semantic approach for query head--modifier detection. First, we mine a large number of instance level head--modifier pairs from search log. Then, we develop a conceptualization mechanism to generalize the instance level pairs to concept level. Finally, we derive weighted concept patterns that are concise, accurate, and have strong generalization power in head--modifier detection. The developed mechanism has been used in production for search relevance and ads matching. We use extensive experiment results to demonstrate the effectiveness of our approach.

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization for more effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graph-based method to iteratively update user- and product-related distributions more reliably in a heterogeneous user--product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from J ing D ong , the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.

We address the problem of finding query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query. We assume that the important aspects of a query are usually presented and repeated in the query’s top retrieved documents in the style of lists, and query facets can be mined out by aggregating these significant lists. We propose a systematic solution, which we refer to as QDMiner, to automatically mine query facets by extracting and grouping frequent lists from free text, HTML tags, and repeat regions within top search results. Experimental results show that a large number of lists do exist and useful query facets can be mined by QDMiner. We further analyze the problem of list duplication, and find better query facets can be mined by modeling fine-grained similarities between lists and penalizing the duplicated lists.

Presents the introductory editorial for this issue of the publication.

Travel planning and recommendation are important aspects of transportation. We propose and investigate a novel Collective Travel Planning (CTP) query that finds the lowest-cost route connecting multiple sources and a destination, via at most$k$meeting points. When multiple travelers target the same destination (e.g., a stadium or a theater), they may want to assemble at meeting points and then go together to the destination by public transport to reduce their global travel cost (e.g., energy, money, or greenhouse-gas emissions). This type of functionality holds the potential to bring significant benefits to society and the environment, such as reducing energy consumption and greenhouse-gas emissions, enabling smarter and greener transportation, and reducing traffic congestions. The CTP query is Max SNP-hard. To compute the query efficiently, we develop two algorithms, including an exact algorithm and an approximation algorithm. The exact algorithm is capable finding the optimal result for small values of$k$(e.g.,$k = 2$) in interactive time, while the approximation algorithm, which has a$5$-approximation ratio, is suitable for other situations. The performance of the CTP query is studied experimentally with real and synthetic spatial data.

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce Web sites support the mechanism of social login where users can sign on the Web sites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product Web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce Web sites to users at social networking sites in “cold-start” situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce Web sites (users who have social networking accounts and have made purchases on e-commerce Web sites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce Web sites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

The proliferation of location-based social networks, such as Foursquare and Facebook Places, offers a variety of ways to record human mobility, including user generated geo-tagged contents, check-in services, and mobile apps. Although trajectory data is of great value to many applications, it is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility, which is affected by multiple contextual information. In this paper, we propose a Multi-Context Trajectory Embedding Model, called MC-TEM, to explore contexts in a systematic way. MC-TEM is developed in the distributed representation learning framework, and it is flexible to characterize various kinds of useful contexts for different applications. To the best of our knowledge, it is the first time that the distributed representation learning methods apply to trajectory data. We formally incorporate multiple context information of trajectory data into the proposed model, including user-level, trajectory-level, location-level, and temporal contexts. All the context information is represented in the same embedding space. We apply MC-TEM to two challenging tasks, namely location recommendation and social link prediction. We conduct extensive experiments on three real-world datasets. Extensive experiment results have demonstrated the superiority of our MC-TEM model over several state-of-the-art methods.

With the pervasiveness of location-based social networks, it becomes increasingly important to consider the social characteristics of locations shared among persons. Several studies have been proposed to infer social strength by using trajectory similarity. However, these studies have two major shortcomings. First, they rely on the explicit co-occurrence of check-in locations. In this situation, a user pair of two friends who seldom share common locations or a user pair of two strangers who heavily share common visited locations will receive an unreliable estimation of the real social strength between them. Second, these studies do not consider how the overall trajectory patterns of users change with the varying of living styles. In this article, we propose a probabilistic generative model to mine latent lifestyle-related patterns from human trajectory data for inferring social strength. It can automatically learnfunctionality topicsconsisting of locations with similar service functions and transition probabilities over the set of functionality topics. Furthermore, a lifestyle is modeled as a unique transition probability matrix over the set of functionality topics. A user has a preference distribution over the set of lifestyles, and he or she is able to select over multiple lifestyles to adapt to different living contexts. The learned lifestyle-related patterns are subsequently used as features in a supervised learner for both strength estimation and link prediction. We conduct extensive experiments to evaluate the performance of the proposed method on two real-world datasets. The experimental results demonstrate the effectiveness of our proposed method.

Representing discrete words in a continuous vector space turns out to be useful for natural language applications related to text understanding. Meanwhile, it poses extensive challenges, one of which is due to the polysemous nature of human language. A common solution (a.k.a word sense induction) is to separate each word into multiple senses and create a representation for each sense respectively. However, this approach is usually computationally expensive and prone to data sparsity, since each sense needs to be managed discriminatively. In this work, we propose a new framework for generating context-aware text representations without diving into the sense space. We model the concept space shared among senses, resulting in a framework that is efficient in both computation and storage. Specifically, the framework we propose is one that: i) projects both words and concepts into the same vector space; ii) obtains unambiguous word representations that not only preserve the uniqueness among words, but also reflect their context-appropriate meanings. We demonstrate the effectiveness of the framework in a number of tasks on text understanding, including word/phrase similarity measurements, paraphrase identification and question-answer relatedness classification.

A large percentage of queries issued to search engines are broad or ambiguous. Search result diversification aims to solve this problem, by returning diverse results that can fulfill as many different information needs as possible. Most existing intent-aware search result diversification algorithms formulate user intents for a query as a flat list of subtopics. In this paper, we introduce a new hierarchical structure to represent user intents and propose two general hierarchical diversification models to leverage hierarchical intents. Experimental results show that our hierarchical diversification models outperform state-of-the-art diversification methods that use traditional flat subtopics.

Humans understand the world by classifying objects into an appropriate level of categories. This process is often automatic and subconscious. Psychologists and linguists call it as Basic-level Categorization (BLC). BLC can benefit lots of applications such as knowledge panel, advertising and recommendation. However, how to quantify basic-level concepts is still an open problem. Recently, much work focuses on constructing knowledge bases or semantic networks from web scale text corpora, which makes it possible for the first time to analyze computational approaches for deriving BLC. In this paper, we introduce a method based on typicality and PMI for BLC. We compare it with a few existing measures such as NPMI and commute time to understand its essence, and conduct extensive experiments to show the effectiveness of our approach. We also give a real application example to show how BLC can help sponsored search.

A persistent data structure, also known as a multiversion data structure in the database literature, is a data structure that preserves all its previous versions as it is updated over time. Every update (inserting, deleting, or changing a data record) to the data structure creates a new version, while all the versions are kept in the data structure so that any previous version can still be queried.

The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects$O$(e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route$q$, if a cluster$c$has high spatial-object density and is spatially close to$q$, it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scanned from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data.

In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo!Answers, where social roles on Twitter include “originators” and “propagators”, and roles on cQA are “askers” and “answerers”. Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author's research expertise area is considered as a social role. A novel application of detecting users' research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model.

Compression algorithms are important for data-oriented tasks, especially in the era of “Big Data.” Modern processors equipped with powerful SIMD instruction sets provide us with an opportunity for achieving better compression performance. Previous research has shown that SIMD-based optimizations can multiply decoding speeds. Following these pioneering studies, we propose a general approach to accelerate compression algorithms. By instantiating the approach, we have developed several novel integer compression algorithms, called Group-Simple, Group-Scheme, Group-AFOR, and Group-PFD, and implemented their corresponding vectorized versions. We evaluate the proposed algorithms on two public TREC datasets, a Wikipedia dataset, and a Twitter dataset. With competitive compression ratios and encoding speeds, our SIMD-based algorithms outperform state-of-the-art nonvectorized algorithms with respect to decoding speeds.

Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users' influence scores. They rarely consider a person's expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally “Sina microblogging”). We found that there is a strong correlation between expertise levels and social media influence scores. In addition, different expertise levels showed influence variation patterns: high-expertise celebrities have stronger influence on the “audience” in their expertise domains.

Within-Network Classification (WNC) techniques are designed for applications where objects to be classified and those with known labels are interlinked. For WNC tasks like web page classification, the homophily principle succeeds by assuming that linked objects, represented as adjacent vertices in a network, are likely to have the same labels. However, in other tasks like chemical structure completion, recent works suggest that the label of a vertex should be related to the local structure it resides in, rather than equated with those of its neighbors. These works also propose structure-aware vertex features or methods to deal with such an issue.

Most existing approaches for text classification represent texts as vectors of words, namely ``Bag-of-Words.'' This text representation results in a very high dimensionality of feature space and frequently suffers from surface mismatching. Short texts make these issues even more serious, due to their shortness and sparsity. In this paper, we propose using ``Bag-of-Concepts'' in short text representation, aiming to avoid the surface mismatching and handle the synonym and polysemy problem. Based on ``Bag-of-Concepts,'' a novel framework is proposed for lightweight short text classification applications. By leveraging a large taxonomy knowledgebase, it learns a concept model for each category, and conceptualizes a short text to a set of relevant concepts. A concept-based similarity mechanism is presented to classify the given short text to the most similar category. One advantage of this mechanism is that it facilitates short text ranking after classification, which is needed in many applications, such as query or ad recommendation. We demonstrate the usage of our proposed framework through a real online application: Channel-based Query Recommendation. Experiments show that our framework can map queries to channels with a high degree of precision (avg. precision=90.3%), which is critical for recommendation applications.

Over the years, frequent subgraphs have been an important kind of targeted pattern in pattern mining research, where most approaches deal with databases holding a number of graph transactions, e.g., the chemical structures of compounds. These methods rely heavily on the downward-closure property (DCP) of the support measure to ensure an efficient pruning of the candidate patterns. When switching to the emerging scenario of single-graph databases such as Google's Knowledge Graph and Facebook's social graph, the traditional support measure turns out to be trivial (either 0 or 1). However, to the best of our knowledge, all attempts to redefine a single-graph support have resulted in measures that either lose DCP, or are no longer semantically intuitive. This paper targets pattern mining in the single-graph setting. We propose mining a new class of patterns called frequent neighborhood patterns, which is free from the "DCP-intuitiveness" dilemma of mining frequent subgraphs in a single graph. A neighborhood is a specific topological pattern in which a vertex is embedded, and the pattern is frequent if it is shared by a large portion (above a given threshold) of vertices. We show that the new patterns not only maintain DCP, but also have equally significant interpretations as subgraph patterns. Experiments on real-life datasets support the feasibility of our algorithms on relatively large graphs, as well as the capability of mining interesting knowledge that is not discovered by prior methods.

This paper tackles the problem of mining subgoals of a given search goal from data. For example, when a searcher wants to travel to London, she may need to accomplish several subtasks such as "book flights," "book a hotel," "find good restaurants" and "decide which sightseeing spots to visit." As another example, if a searcher wants to lose weight, there may exist several alternative solutions such as "do physical exercise," "take diet pills," and "control calorie intake." In this paper, we refer to such subtasks or solutions as subgoals, and propose to utilize sponsored search data for finding subgoals of a given query by means of query clustering. Advertisements (ads) reflect advertisers' tremendous efforts in trying to match a given query with implicit user needs. Moreover, ads are usually associated with a particular action or transaction. We therefore hypothesized that they are useful for subgoal mining. To our knowledge, our work is the first to use sponsored search data for this purpose. Our experimental results show that sponsored search data is a good resource for obtaining related queries and for identifying subgoals via query clustering. In particular, our method that combines ad impressions from sponsored search data and query co-occurrences from session data outperforms a state-of-the-art query clustering method that relies on document clicks rather than ad impressions in terms of purity, NMI, Rand Index, F1-measure and subgoal recall.

A diversified search result for an underspecified query generally contains web pages in which there are answers that are relevant to different aspects of the query. In order to help the user locate such relevant answers, we propose a simple extension to the standard Search Engine Result Page (SERP) interface, called AspecTiles. In addition to presenting a ranked list of URLs with their titles and snippets, AspecTiles visualizes the relevance degree of a document to each aspect by means of colored squares ("tiles"). To compare AspecTiles with the standard SERP interface in terms of usefulness, we conducted a user study involving 30 search tasks designed based on the TREC web diversity task topics as well as 32 participants. Our results show that AspecTiles has some advantages in terms of search performance, user behavior, and user satisfaction. First, AspecTiles enables the user to gather relevant information significantly more efficiently than the standard SERP interface for tasks where the user considers several different aspects of the query to be important at the same time (multi-aspect tasks). Second, AspecTiles affects the user's information seeking behavior: with this interface, we observed significantly fewer query reformulations, shorter queries and deeper examinations of ranked lists in multi-aspect tasks. Third, participants of our user study found AspecTiles significantly more useful for finding relevant information and easy to use than the standard SERP interface. These results suggest that simple interfaces like AspecTiles can enhance the search performance and search experience of the user when their queries are underspecified.

Query suggestion is a useful tool to help users formulate better queries. Although this has been found highly useful globally, its effect on different queries may vary. In this paper, we examine the impact of query suggestion on queries of different degrees of difficulty. It turns out that query suggestion is much more useful for difficult queries than easy queries. In addition, the suggestions for difficult queries should rely less on their similarity to the original query. In this paper, we use a learning-to-rank approach to select query suggestions, based on several types of features including a query performance prediction. As query suggestion has different impacts on different queries, we propose an adaptive suggestion approach that makes suggestions only for difficult queries. We carry out experiments on real data from a search engine. Our results clearly indicate that an approach targeting difficult queries can bring higher gain than a uniform suggestion approach.

Query suggestion is a useful tool to help users express their information needs by supplying alternative queries. When evaluating the effectiveness of query suggestion algorithms, many previous studies focus on measuring whether a suggestion query is relevant or not to the input query. This assessment criterion is too simple to describe users' requirements. In this paper, we introduce two scenarios of query suggestion. The first scenario represents cases where the search result of the input query is unsatisfactory. The second scenario represents cases where the search result is satisfactory but the user may be looking for alternative solutions. Based on the two scenarios, we propose two assessment criteria. Our labeling results indicate that the new assessment criteria provide finer distinctions among query suggestions than the traditional relevance-based criterion.

Conventional search engines usually return a ranked list of web pages in response to a query. Users have to visit several pages to locate the relevant parts. A promising future search scenario should involve: (1) understanding user intents; (2) providing relevant information directly to satisfy searchers' needs, as opposed to relevant pages. In this paper, we present a search paradigm to summarize a query's information from different aspects. Query aspects could be aligned to user intents. The generated summaries for query aspects are expected to be both specific and informative, so that users can easily and quickly find relevant information. Specifically, we use a Composite Query for Summarization" method, where a set of component queries are used for providing additional information for the original query. The system leverages the search engine to proactively gather information by submitting multiple component queries according to the original query and its aspects. In this way, we could get more relevant information for each query aspect and roughly classify information. By comparative mining the search results of different component queries, it is able to identify query (dependent) aspect words, which help to generate more specific and informative summaries. The experimental results on two data sets, Wikipedia and TREC ClueWeb2009, are encouraging. Our method outperforms two baseline methods on generating informative summaries.

We address the problem of finding multiple groups of words or phrases that explain the underlying query facets, which we refer to as query dimensions. We assume that the important aspects of a query are usually presented and repeated in the query's top retrieved documents in the style of lists, and query dimensions can be mined out by aggregating these significant lists. Experimental results show that a large number of lists do exist in the top results, and query dimensions generated by grouping these lists are useful for users to learn interesting knowledge about the queries.

This paper introduces the problem of matching people names to their corresponding social network identities such as their Twitter accounts. Existing tools for this purpose build upon naive textual matching and inevitably suffer low precision, due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage evidences extracted from the Web corpus. In particular, as such an example, weadopt Web document co-occurrences, which can be interpreted as an implicit counterpart of Twitter follower relationships. Using both textual and relational features, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we train a separate classifier, using not only the top relevance score but also various statistical features extracted from the relevance scores of all candidates, and empirically validate to outperform the baseline approach. We evaluate our proposed system using real-life internetscale entity-relationship and social network graphs.

Most existing search result diversification algorithms diversify search results in terms of a specific dimension. In this paper, we argue that search results should be diversified in a multi-dimensional way, as queries are usually ambiguous at different levels and dimensions. We first explore mining subtopics from four types of data sources, including anchor texts, query logs, search result clusters, and web sites. Then we propose a general framework that explicitly diversifies search results based on multiple dimensions of subtopics. It balances the relevance of documents with respect to the query and the novelty of documents by measuring the coverage of subtopics. Experimental results on the TREC 2009 Web track dataset indicate that combining multiple types of subtopics do help better understand user intents. By incorporating multiple types of subtopics, our models improve the diversity of search results over the sole use of one of them, and outperform two state-of-the-art models.

There has been a large amount of research on early termination techniques in web search and information retrieval. Such techniques return the top-k documents without scanning and evaluating the full inverted lists of the query terms. Thus, they can greatly improve query processing efficiency. However, only a limited amount of efficient top-k processing work considers the impact of term proximity, i.e., the distance between term occurrences in a document, which has recently been integrated into a number of retrieval models to improve effectiveness.

We demonstrate Product EntityCube, a product recommendation and navigation system. While the unprecedented scale of a product search portal enables to satisfy users with diverse needs, this scale also complicates product recommendation. Specifically, our target application poses a unique challenge of overcoming insufficient user profiles and feedbacks. To address this problem, we organize query results into clusters representing different user perceptions of similarity, and provide a navigational UI to handle personal interests. Specifically, we first discuss hybrid object clustering capturing diverse user interests from millions of Web pages and disambiguating different perceptions using feature-based similarity. We then discuss skyline object ranking to highlight interesting items at each cluster. Our demonstration illustrates how Product EntityCube can enrich user product shopping experiences.

Internet users regularly have the need to find biographies and facts of people of interest. Wikipedia has become the first stop for celebrity biographies and facts. However, Wikipedia can only provide information for celebrities because of its neutral point of view (NPOV) editorial policy. In this paper we propose an integrated bootstrapping framework named BioSnowball to automatically summarize the Web to generate Wikipedia-style pages for any person with a modest web presence. In BioSnowball, biography ranking and fact extraction are performed together in a single integrated training and inference process using Markov Logic Networks (MLNs) as its underlying statistical model. The bootstrapping framework starts with only a small number of seeds and iteratively finds new facts and biographies. As biography paragraphs on the Web are composed of the most important facts, our joint summarization model can improve the accuracy of both fact extraction and biography ranking compared to decoupled methods in the literature. Empirical results on both a small labeled data set and a real Web-scale data set show the effectiveness of BioSnowball. We also empirically show that BioSnowball outperforms the decoupled methods.

There has been a large amount of research on efficient document retrieval in both IR and web search areas. One important technique to improve retrieval efficiency is early termination, which speeds up query processing by avoiding scanning the entire inverted lists. Most early termination techniques first build new inverted indexes by sorting the inverted lists in the order of either the term-dependent information, e.g., term frequencies or term IR scores, or the term-independent information, e.g., static rank of the document; and then apply appropriate retrieval strategies on the resulting indexes. Although the methods based only on the static rank have been shown to be ineffective for the early termination, there are still many advantages of using the methods based on term-independent information. In this paper, we propose new techniques to organize inverted indexes based on the term-independent information beyond static rank and study the new retrieval strategies on the resulting indexes. We perform a detailed experimental evaluation on our new techniques and compare them with the existing approaches. Our results on the TREC GOV and GOV2 data sets show that our techniques can improve query efficiency significantly.

The two most important tasks in information extraction from the Web are webpage structure understanding and natural language sentences processing. However, little work has been done toward an integrated statistical model for understanding webpage structures and processing natural language sentences within the HTML elements. Our recent work on webpage understanding introduces a joint model of Hierarchical Conditional Random Fields (HCRFs) and extended Semi-Markov Conditional Random Fields (Semi-CRFs) to leverage the page structure understanding results in free text segmentation and labeling. In this top-down integration model, the decision of the HCRF model could guide the decision making of the Semi-CRF model. However, the drawback of the top-down integration strategy is also apparent, i.e., the decision of the Semi-CRF model could not be used by the HCRF model to guide its decision making. This paper proposed a novel framework called WebNLP, which enables bidirectional integration of page structure understanding and text understanding in an iterative manner. We have applied the proposed framework to local business entity extraction and Chinese person and organization name extraction. Experiments show that the WebNLP framework achieved significantly better performance than existing methods.

Different queries require different ranking methods. It is however challenging to determine what queries are similar, and how to rank documents for them. In this paper, we propose a new method to cluster queries according to the similarity determined based on URLs in their answers. We then train specific ranking models for each query cluster. In addition, a cluster-specific measure of authority is defined to favor documents from authoritative websites on the corresponding topics. The proposed approach is tested using data from a search engine. It turns out that our proposed topic-dependent models can significantly improve the search results of eight most popular categories of queries.

Mainstream link-based static-rank algorithms (e.g. PageRank and its variants) express the importance of a page as the linear combination of its in-links and compute page importance scores by solving a linear system in an iterative way. Such linear algorithms, however, may give apparently unreasonable static-rank results for some link structures. In this paper, we examine the static-rank computation problem from the viewpoint of evidence combination and build a probabilistic model for it. Based on the model, we argue that a nonlinear formula should be adopted, due to the correlation or dependence between links. We focus on examining some simple formulas which only consider the correlation between links in the same domain. Experiments conducted on 100 million web pages (with multiple static-rank quality evaluation metrics) show that higher quality static-rank could be yielded by the new nonlinear algorithms. The convergence of the new algorithms is also proved in this paper by nonlinear functional analysis.

Web information is often presented in the form of record, e.g., a product record on a shopping website or a personal profile on a social utility website. Given a host webpage and related information needs, how to identify relevant records as well as their internal semantic structures is critical to many online information systems. Wrapper induction is one of the most effective methods for such tasks. However, most traditional wrapper techniques have issues dealing with web records since they are designed to extract information from a page, not a record. We propose a record-level wrapper system. In our system, we use a novel ``broom'' structure to represent both records and generated wrappers. With such representation, our system is able to effectively extract records and identify their internal semantics at the same time. We test our system on 16 real-life websites from four different domains. Experimental results demonstrate 99\% extraction accuracy in terms of F1-Value.

Query result clustering has recently attracted a lot of attention to provide users with a succinct overview of relevant results. However, little work has been done on organizing the query results for object-level search. Object-level search result clustering is challenging because we need to support diverse similarity notions over object-specific features (such as the price and weight of a product) of heterogeneous domains. To address this challenge, we propose a hybrid subspace clustering algorithm called Hydra. Algorithm Hydra captures the user perception of diverse similarity notions from millions of Web pages and disambiguates different senses using feature-based subspace locality measures. Our proposed solution, by combining wisdom of crowds and wisdom of data, achieves robustness and efficiency over existing approaches. We extensively evaluate our proposed framework and demonstrate how to enrich user experiences in object-level search using a real-world product search scenarios.

As a good complement to page content, anchor texts have been extensively used, and proven to be useful, in commercial search engines. However, anchor texts have been assumed to be independent, whether they come from the same Web site or not. Intuitively, an anchor text from unrelated Web sites should be considered as stronger evidence than that from the same site. This paper proposes two new methods to take into account the possible relationships between anchor texts. We consider two relationships in this paper: links from the same site and links from related sites. The importance assigned to the anchor texts in these two situations is discounted. Experimental results show that these two new models outperform the baseline model which assumes independence between hyperlinks.

Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples. Bootstrapping systems significantly reduce the number of training examples, but they usually apply heuristic-based methods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Furthermore, existing bootstrapping methods do not perform open information extraction (Open IE), which can identify various types of relations without requiring pre-specifications. In this paper, we propose a statistical extraction framework called Statistical Snowball (StatSnowball), which is a bootstrapping system and can perform both traditional relation extraction and Open IE.

Although personalized search has been under way for many years and many personalization algorithms have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users and under different search contexts. In this paper, we study this problem and provide some findings. We present a large-scale evaluation framework for personalized search based on query logs and then evaluate five personalized search algorithms (including two click-based ones and three topical-interest-based ones) using 12-day query logs of Windows Live Search. By analyzing the results, we reveal that personalized Web search does not work equally well under various situations. It represents a significant improvement over generic Web search for some queries, while it has little effect and even harms query performance under some situations. We propose click entropy as a simple measurement on whether a query should be personalized. We further propose several features to automatically predict when a query will benefit from a specific personalization algorithm. Experimental results show that using a personalization algorithm for queries selected by our prediction model is better than using it simply for all queries.

Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examples is to employ human experts to judge the relevance of documents. Unfortunately, it is difficult, time-consuming and costly. In this paper, we study the problem of exploiting click-through data for learning web search rankings that can be collected at much lower cost. We extract pairwise relevance preferences from a large-scale aggregated click-through dataset, compare these preferences with explicit human judgments, and use them as training examples to learn ranking functions. We find click-through data are useful and effective in learning ranking functions. A straightforward use of aggregated click-through data can outperform human judgments. We demonstrate that the strategies are only slightly affected by fraudulent clicks. We also reveal that the pairs which are very reliable, e.g., the pairs consisting of documents with large click frequency differences, are not sufficient for learning.

Every piece of textual data is generated as a method to convey its authors' opinion regarding specific topics. Authors deliberately organize their writings and create links, i.e., references, acknowledgments, for better expression. Thereafter, it is of interest to study texts as well as their relations to understand the underlying topics and communities. Although many efforts exist in the literature in data clustering and topic mining, they are not applicable to community discovery on large document corpus for several reasons. First, few of them consider both textual attributes as well as relations. Second, scalability remains a significant issue for large-scale datasets. Additionally, most algorithms rely on a set of initial parameters that are hard to be captured and tuned. Motivated by the aforementioned observations, a hierarchical community model is proposed in the paper which distinguishes community cores from affiliated members. We present our efforts to develop a scalable community discovery solution for large-scale document corpus. Our proposal tries to quickly identify potential cores as seeds of communities through relation analysis. To eliminate the influence of initial parameters, an innovative attribute-based core merge process is introduced so that the algorithm promises to return consistent communities regardless initial parameters. Experimental results suggest that the proposed method has high scalability to corpus size and feature dimensionality, with more than 15 topical precision improvement compared with popular clustering techniques.

A semantic class is a collection of items (words or phrases) sharing common semantic properties. This paper proposes an approach to constructing one or multiple semantic classes for an input item. Two challenges are addressed: multi-membership, and noise-tolerance.

Little work has been done towards an integrated statistical model for understanding webpage structures and processing natural language sentences within the HTML elements. This paper proposed a novel framework called WebNLP which enables bidirectional integration of page structure understanding and text understanding in an iterative manner. Experiments show that the WebNLP framework achieved significantly better performance.

Modern web search engines, while indexing billions of web pages, are expected to process queries and return results in a very short time. Many approaches have been proposed for efficiently computing top-k query results, but most of them ignore one key factor in the ranking functions of commercial search engines - term-proximity, which is the metric of the distance between query terms in a document. When term-proximity is included in ranking functions, most of the existing top-k algorithms will become inefficient. To address this problem, in this paper we propose to build a compact phrase index to speed up the search process when incorporating the term-proximity factor. The compact phrase index can help more accurately estimate the score upper bounds of unknown documents. The size of the phrase index is controlled by including a small portion of phrases which are possibly helpful for improving search performance. Phrase index has been used to process phrase queries in existing work. It is, however, to the best of our knowledge, the first time that phrase index is used to improve the performance of generic queries. Experimental results show that, compared with the state-of-the-art top-k computation approaches, our approach can reduce average query processing time to 1/5 for typical setttings.

We present a demonstration of an interactive wrapper induction system, called Pictor, which is able to minimize labeling cost, yet extract data with high accuracy from a website. Our demonstration will introduce two proposed technologies: record-level wrappers and a wrapper-assisted labeling strategy. These approaches allow Pictor to exploit previously generated wrappers, in order to predict similar labels in a partially labeled webpage or a completely new webpage. Our experiment results show the effectiveness of the Pictor system.

Modern web search engines are expected to return top-k results efficiently given a query. Although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignore some especially important factors in ranking functions, e.g. term proximity (the distance relationship between query terms in a document). The inclusion of term proximity breaks the monotonicity of ranking functions and therefore leads to additional challenges for efficient query processing. This paper studies the performance of some existing top-k computation approaches using term-proximity-enabled ranking functions. Our investigation demonstrates that, when term proximity is incorporated into ranking functions, most existing index structures and top-k strategies become quite inefficient. According to our analysis and experimental results, we propose two index structures and their corresponding index pruning strategies: Structured and Hybrid, which performs much better on the new settings. Moreover, the efficiency of index building and maintenance would not be affected too much with the two approaches.

Many websites have large collections of pages generated dynamically from an underlying structured source like a database. The data of a category are typically encoded into similar pages by a common script or template. In recent years, some value-added services, such as comparison shopping and vertical search in a specific domain, have motivated the research of extraction technologies with high accuracy. Almost all previous works assume that input pages of a wrapper induction system conform to a common template and they can be easily identified in terms of a common schema of URL. However, we observed that it is hard to distinguish different templates using dynamic URLs today. Moreover, since extraction accuracy heavily depends on how consistent input pages are, we argue that it is risky to determine whether pages share a common template solely based on URLs. Instead, we propose a new approach that utilizes similarity between pages to detect templates. Our approach separates pages with notable inner differences and then generates wrappers, respectively. Experimental results show that our proposed approach is feasible and effective for improving extraction accuracy.

Recent work has shown the effectiveness of leveraging layout and tag-tree structure for segmenting webpages and labeling HTML elements. However, how to effectively segment and label the text contents inside HTML elements is still an open problem. Since many text contents on a webpage are often text fragments and not strictly grammatical, traditional natural language processing techniques, that typically expect grammatical sentences, are no longer directly applicable. In this paper, we examine how to use layout and tag-tree structure in a principled way to help understand text contents on webpages. We propose to segment and label the page structure and the text content of a webpage in a joint discriminative probabilistic model. In this model, semantic labels of page structure can be leveraged to help text content understanding, and semantic labels ofthe text phrases can be used in page structure understanding tasks such as data record detection. Thus, integration of both page structure and text content understanding leads to an integrated solution of webpage understanding. Experimental results on research homepage extraction show the feasibility and promise of our approach.

To explore various ideas and algorithms for improving relevance of a search engine, we found it necessary to build an infrastructure to provide large-scale data management and data processing capabilities. WebStudio is an infrastructure we have constructed to provide an integrated development environment (IDE) for researchers and developers to use in quickly building prototypes and conducting experiments at Web-scale. It is also a Web data management system to allow users to easily store, access, and manipulate Web data.

Although personalized search has been proposed for many years and many personalization strategies have been investigated, it is still unclear whether personalization is consistently effective on different queries for different users, and under different search contexts. In this paper, we study this problem and get some preliminary conclusions. We present a large-scale evaluation framework for personalized search based on query logs, and then evaluate five personalized search strategies (including two click-based and three profile-based ones) using 12-day MSN query logs. By analyzing the results, we reveal that personalized search has significant improvement over common web search on some queries but it also has little effect on other queries (e.g., queries with small click entropy). It even harms search accuracy under some situations. Furthermore, we show that straightforward click-based personalization strategies perform consistently and considerably well, while profile-based ones are unstable in our experiments. We also reveal that both long-term and short-term contexts are very important in improving search performance for profile-based personalized search strategies.

The primary function of current Web search engines is essentially relevance ranking at the document level. However, myriad structured information about real-world objects embedded in static Web pages and online Web databases. In this paper, we propose a paradigm shift to enable searching at the object level. In traditional information retrieval models, documents are taken as the retrieval units and the content of a document is considered reliable. However, this reliability assumption is no longer valid in the object retrieval context when multiple copies of information about the same object typically exist. These copies may be inconsistent because of diversity of Web site qualities and the limited performance of current information extraction techniques. In this paper, we propose several language models for Web object retrieval. We test these models on our academic search engine called Libra and compare their performances. 1.

It is widely believed that some queries submitted to search engines are by nature ambiguous (e.g., java, apple). However, few studies have investigated the questions of "how many queries are ambiguous?" and "how can we automatically identify an ambiguous query?" This paper deals with these issues. First, we construct the taxonomy of query ambiguity, and ask human annotators to manually classify queries based upon it. From manually labeled results, we find that query ambiguity is to some extent predictable. We then use a supervised learning approach to automatically classify queries as being ambiguous or not. Experimental results show that we can correctly identify 87% of labeled queries. Finally, we estimate that about 16% of queries in a real search log are ambiguous.

This paper examines the problem of utilizing pseudo-anchor text to help ranking Web objects in vertical search. We adopt a machine learning based approach to extract pseudo-anchor text for a vertical object from its candidate anchor blocks. Experiments in academic search domain indicate that our approach is able to dramatically improve search performance.

Extracting and integrating object information from the Web is of great significance for Web data management. The existing Web information extraction techniques cannot provide satisfactory solution to the Web object extraction task since objects of the same type are distributed in diverse Web sources, whose structures are highly heterogeneous. In this paper, we propose a novel approach called Object-Level Information Extraction (OLIE) to extract Web objects. This approach extends a classic information extraction algorithm, Conditional Random Fields (CRF), by adding Web-specific information. The experimental results show OLIE can significantly improve the Web object extraction accuracy.

The high quality, structured data from Web structured sources is invaluable for many applications. Hidden Web databases are not directly crawlable by Web search engines and are only accessible through Web query forms or via Web service interfaces. Recent research efforts have been focusing on understanding these Web query forms. A critical but still largely unresolved question is: how to efficiently acquire the structured information inside Web databases through iteratively issuing meaningful queries? In this paper we focus on the central issue of enabling efficient Web database crawling through query selection, i.e. how to select good queries to rapidly harvest data records from Web databases. We model each structured Web database as a distinct attribute-value graph. Under this theoretical framework, the database crawling problem is transformed into a graph traversal one that follows "relational" links. We show that finding an optimal query selection plan is equivalent to finding a Minimum Weighted Dominating Set of the corresponding database graph, a well-known NP-Complete problem. We propose a suite of query selection techniques aiming at optimizing the query harvest rate. Extensive experimental evaluations over real Web sources and simulations over controlled database servers validate the effectiveness of our techniques and provide insights for future efforts in this

Recent work has shown the feasibility and promise of templateindependent Web data extraction. However, existing approaches use decoupled strategies – attempting to do data record detection and attribute labeling in two separate phases. In this paper, we show that separately extracting data records and attributes is highly ineffective and propose a probabilistic model to perform these two tasks simultaneously. In our approach, record detection can benefit from the availability of semantics required in attribute labeling and, at the same time, the accuracy of attribute labeling can be improved when data records are labeled in a collective manner. The proposed model is called Hierarchical Conditional Random Fields. It can efficiently integrate all useful features by learning their importance, and it can also incorporate hierarchical interactions which are very important for Web data extraction. We empirically compare the proposed model with existing decoupled approaches for product information extraction, and the results show significant improvements in both record detection and attribute labeling.

Understanding goals and preferences behind a user's online activities can greatly help information providers, such as search engine and E-Commerce web sites, to personalize contents and thus improve user satisfaction. Understanding a user's intention could also provide other business advantages to information providers. For example, information providers can decide whether to display commercial content based on user's intent to purchase. Previous work on Web search defines three major types of user search goals for search queries: navigational, informational and transactional or resource [1][7]. In this paper, we focus our attention on capturing commercial intention from search queries and Web pages, i.e., when a user submits the query or browse a Web page, whether he/she is about to commit or in the middle of a commercial activity, such as purchase, auction, selling, paid service, etc. We call the commercial intentions behind a user's online activities as OCI (Online Commercial Intention). We also propose the notion of "Commercial Activity Phase" (CAP), which identifies in which phase a user is in his/her commercial activities: Research or Commit. We present the framework of building machine learning models to learn OCI based on any Web page content. Based on that framework, we build models to detect OCI from search queries and Web pages. We train machine learning models from two types of data sources for a given search query: content of algorithmic search result page(s) and contents of top sites returned by a search engine. Our experiments show that the model based on the first data source achieved better performance. We also discover that frequent queries are more likely to have commercial intention. Finally we propose our future work in learning richer commercial intention behind users' online activities.

This paper proposes GBM (gravitation-based model), a physical model for information retrieval inspired by Newton's theory of gravitation. A mapping is built in this model from concepts of information retrieval (documents, queries, relevance, etc) to those of physics (mass, distance, radius, attractive force, etc). This model actually provides a new perspective on IR problems. A family of effective term weighting functions can be derived from it, including the well-known BM25 formula. This model has some advantages over most existing ones: First, because it is directly based on basic physical laws, the derived formulas and algorithms can have their explicit physical interpretation. Second, the ranking formulas derived from this model satisfy more intuitive heuristics than most of existing ones, thus have the potential to behave empirically better and to be used safely on various settings. Finally, a new approach for structured document retrieval derived from this model is more reasonable and behaves better than existing ones.

In contrast with the current Web search methods that essentially do document-level ranking and retrieval, we are exploring a new paradigm to enable Web search at the object level. We collect Web information for objects relevant for a specific application domain and rank these objects in terms of their relevance and popularity to answer user queries. Traditional PageRank model is no longer valid for object popularity calculation because of the existence of heterogeneous relationships between objects. This paper introduces PopRank, a domain-independent object-level link analysis model to rank the objects within a specific domain. Specifically we assign a popularity propagation factor to each type of object relationship, study how different popularity propagation factors for these heterogeneous relationships could affect the popularity ranking, and propose efficient approaches to automatically decide these factors. Our experiments are done using 1 million CS papers, and the experimental results show that PopRank can achieve significantly better ranking results than naively applying PageRank on the object graph.

Link Analysis has shown great potential in improving the performance of web search. PageRank and HITS are two of the most popular algorithms. Most of the existing link analysis algorithms treat a web page as a single node in the web graph. However, in most cases, a web page contains multiple semantics and hence the web page might not be considered as the atomic node. In this paper, the web page is partitioned into blocks using the vision-based page segmentation algorithm. By extracting the page-to-block, block-to-page relationships from link structure and page layout analysis, we can construct a semantic graph over the WWW such that each node exactly represents a single semantic topic. This graph can better describe the semantic structure of the web. Based on block-level link analysis, we proposed two new algorithms, Block Level PageRank and Block Level HITS, whose performances we study extensively using web data.

Multiple-topic and varying-length of web pages are two negative factors significantly affecting the performance of web search. In this paper, we explore the use of page segmentation algorithms to partition web pages into blocks and investigate how to take advantage of block-level evidence to improve retrieval performance in the web context. Because of the special characteristics of web pages, different page segmentation method will have different impact on web search performance. We compare four types of methods, including fixed-length page segmentation, DOM-based page segmentation, vision-based page segmentation, and a combined method which integrates both semantic and fixed-length properties. Experiments on block-level query expansion and retrieval are performed. Among the four approaches, the combined method achieves the best performance for web search. Our experimental results also show that such a semantic partitioning of web pages effectively deals with the problem of multiple drifting topics and mixed lengths, and thus has great potential to boost up the performance of current web search engines.

Contextual retrieval is a critical technique for facilitating many important applications such as mobile search, personalized search, PC troubleshooting, etc. Despite of its importance, there is no comprehensive retrieval model to describe the contextual retrieval process. We observed that incompatible context, noisy context and incomplete query are several important issues commonly existing in contextual retrieval applications. However, these issues have not been previously explored and discussed. In this paper, we propose probabilistic models to address these problems. Our study clearly shows that query log is the key to build effective contextual retrieval models. We also conduct a case study in the PC troubleshooting domain to testify the performance of the proposed models and experimental results show that the models can achieve very good retrieval precision.

Previous work shows that a web page can be partitioned into multiple segments or blocks, and often the importance of those blocks in a page is not equivalent. Also, it has been proven that differentiating noisy or unimportant blocks from pages can facilitate web mining, search and accessibility. However, no uniform approach and model has been presented to measure the importance of different segments in web pages. Through a user study, we found that people do have a consistent view about the importance of blocks in web pages. In this paper, we investigate how to find a model to automatically assign importance values to blocks in a web page. We define the block importance estimation as a learning problem. First, we use a vision-based page segmentation algorithm to partition a web page into semantic blocks with a hierarchical structure. Then spatial features (such as position and size) and content features (such as the number of images and links) are extracted to construct a feature vector for each block. Based on these features, learning algorithms are used to train a model to assign importance to different segments in the web page. In our experiments, the best model can achieve the performance with Micro-F1 79% and Micro-Accuracy 85.9%, which is quite close to a person's view.

Various models and approaches have been proposed for mapping XML data into relational tables recently. Most of those approaches produce relational schema for given XML data, based on pre-defined rules, heuristics, and user specifications, without considering workload As the result, the schema obtained is often not optimal with respect to query performance. In this paper, we present a cost-driven approach to generate a near-optimal relational schema fir a given XML data and expected workload, in the presence of space constraint. An efficient heuristic algorithm based on Hill Climbing is proposed together with a set of state transformation operations. Experimental study using the prototype system implementing the proposed algorithm, indicates that the produced schema can provide better performance than those well-known mapping approaches published in the literature.

In contrast to traditional document retrieval, a web page as a whole is not a good information unit to search because it often contains multiple topics and a lot of irrelevant information from navigation, decoration, and interaction part of the page. In this paper, we propose a VIsion-based Page Segmentation (VIPS) algorithm to detect the semantic content structure in a web page. Compared with simple DOM based segmentation method, our page segmentation scheme utilizes useful visual cues to obtain a better partition of a page at the semantic level. By using our VIPS algorithm to assist the selection of query expansion terms in pseudo-relevance feedback in web information retrieval, we achieve 27% performance improvement on Web Track dataset.

Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.

Query expansion has long been suggested as an effective way to resolve the short query and word mismatching problems. A number of query expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web query logs. In this study, we propose a new method for query expansion based on query logs. The central idea is to extract probabilistic correlations between query terms and document terms by analyzing query logs. These correlations are then used to select high-quality expansion terms for new queries. The experimental results show that our log-based probabilistic query expansion method can greatly improve the search performance and has several advantages over other existing methods.

Query clustering is a process used to discover frequently asked questions or most popular topics on a search engine. This process is crucial for search engines based on question-answering. Because of the short lengths of queries, approaches based on keywords are not suitable for query clustering. This paper describes a new query clustering method that makes use of user logs which allow us to identify the documents the users have selected for a query. The similarity between two queries may be deduced from the common documents the users selected for them. Our experiments show that a combination of both keywords and user logs is better than using either method alone.

Query clustering is crucial for automatically discovering frequently asked queries (FAQs) or most popular topics on a question-answering search engine. Due to the short length of queries, the traditional approaches based on keywords are not suitable for query clustering. This paper describes our attempt to cluster similar queries according to their contents as well as the document click information in the user logs.

In order to increase retrieval precision, some new search engines provide manually verified answers to Frequently Asked Queries (FAQs). An underlying task is the identification of FAQs. This paper describes our attempt to cluster similar queries according to their contents as well as user logs. Our preliminary results show that the resulting clusters provide useful information for FAQ identification.