Large language models (LLMs) have been increasingly applied to various domains, which triggers increasing concerns about LLMs' safety on specialized domains, e.g. medicine. Despite prior explorations on general jailbreaking attacks, there are two challenges for applying existing attacks on testing the domain-specific safety of LLMs: (1) Lack of professional knowledge-driven attacks(2) Insufficient coverage of domain knowledge. To bridge this gap, we propose a new task, knowledge-to-jailbreak, which aims to generate jailbreaking attacks from domain knowledge, requiring both attack effectiveness and knowledge relevance. We collect a large-scale dataset with 12,974 knowledge-jailbreak pairs and fine-tune a large language model as jailbreak-generator, to produce domain knowledge-specific jailbreaks. Experiments on 13 domains and 8 target LLMs demonstrate the effectiveness of jailbreak-generator in generating jailbreaks that are both threatening to the target LLMs and relevant to the given knowledge. We also apply our method to an out-of-domain knowledge base, showing that jailbreak-generator can generate jailbreaks that are comparable in harmfulness to those crafted by human experts. Data and code are available at: https://github.com/THU-KEG/Knowledge-to-Jailbreak/.

Retrieval-augmented generation (RAG) has emerged as a promising solution to enhance the reliability of large language models (LLMs) with external knowledge. Existing RAG methods share a common strategy for knowledge injection: they place the retrieved documents into the input context of the LLM, which we refer to as the in-context knowledge injection method. While this approach is simple and often effective, it has inherent limitations. Firstly, increasing the context length and number of relevant documents can lead to higher computational overhead and degraded performance, especially in complex reasoning tasks. More importantly, in-context knowledge injection operates primarily at the input level, but LLMs store their internal knowledge in their parameters. This gap fundamentally limits the capacity of in-context methods. To this end, we introduce Parametric RAG, a new RAG paradigm that integrates external knowledge directly into the feed-forward networks of an LLM through document parameterization. This approach not only reduces online computational costs by shortening the input context length, but also deepens the integration of external knowledge by enabling LLMs to utilize it in the same way as internal parametric knowledge. Experimental results demonstrate that Parametric RAG substantially enhances the effectiveness and efficiency of knowledge augmentation in LLMs. Also, it can be combined with in-context RAG methods to achieve even better performance. We have open-sourced all the code, data, and models in the following GitHub link: https://github.com/oneal2000/PRAG

Retrieval-Augmented Generation (RAG) systems have become a crucial tool to augment large language models (LLMs) with external knowledge for better task performance.However, existing traditional RAG methods inject knowledge directly into the context, resulting in several limitations.First, these methods highly rely on the in-context learning capability of LLMs, which often leads to excessively long contexts.This is inefficient due to the quadratic complexity of self-attention, leading to significant increase in inference time.Second, the extended context and the nature of self-attention can cause the LLMs to lose important information in the context, thereby degrading the original capabilities of LLMs.Third, the effectiveness of knowledge injection is perturbed by the permutation of knowledge within the extended context, reducing the robustness of existing RAG methods.To tackle the above problems, we propose DecoupledRAG, a method that decouples external knowledge from the context within the RAG framework.Specifically, we introduce a cross-attention based method that injects retrieved knowledge directly into the inference process of LLM on the fly, without modifying its parameters or the input context, so that the external knowledge can be utilized robustly in a permutation-independent manner.To the best of our knowledge, this is the first work that explore how to utilize cross-attention to inject knowledge with low training cost in decoder-only LLM era.By leveraging cross-attention operation, DecoupledRAG enables seamless knowledge aggregation without creating extended context.Experimental results demonstrate that our method could achieve

states without platform intervention; 2. offline experiments employing our proposed intervention mechanisms on diverse datasets; and 3. results from a three-week online experiment conducted on Instagram Reels short-video recommendation platform.

This is the second workshop in the series which discusses the cutting-edge developments in research and applications of personalizing large language models (LLMs) and adapting them to the demands of diverse user populations and societal needs. The full-day workshop plan includes several keynotes and invited talks, a poster session and a panel discussion.

This workshop discusses the cutting-edge developments in research and applications of personalizing large language models (LLMs) and adapting them to the demands of diverse user populations and societal needs. The full-day workshop includes several keynotes and invited talks, a poster session and a panel discussion.

Repeated risk minimization is a popular choice in real-world recommender systems driving their recommendation algorithms to adapt to user preferences and trends. However, numerous studies have shown that it exacerbates retention disparities among user groups, resulting in polarization within the user population. Given the primary objective of improving long-term user engagement in most industrial recommender systems and the significant commercial benefits from a diverse user population, enforcing retention fairness across user population is therefore crucial. Nonetheless, this goal is highly challenging due to the unknown dynamics of user retention (e.g., when a user would abandon the system) and the simultaneous aim to maximize the experience of every user.

The Probability Ranking Principle (PRP) has been considered as the foundational standard in the design of information retrieval (IR) systems. The principle requires an IR module's returned list of results to be ranked with respect to the underlying user interests, so as to maximize the results' utility. Nevertheless, we point out that it is inappropriate to indiscriminately apply PRP through every stage of a contemporary IR system. Such systems contain multiple stages (e.g., retrieval, pre-ranking, ranking, and re-ranking stages, as examined in this paper). The selection bias inherent in the model of each stage significantly influences the results that are ultimately presented to users. To address this issue, we propose an improved ranking principle for multi-stage systems, namely the Generalized Probability Ranking Principle (GPRP), to emphasize both the selection bias in each stage of the system pipeline as well as the underlying interest of users. We realize GPRP via a unified algorithmic framework named Full Stage Learning to Rank. Our core idea is to first estimate the selection bias in the subsequent stages and then learn a ranking model that best complies with the downstream modules' selection bias so as to deliver its top ranked results to the final ranked list in the system's output. We performed extensive experiment evaluations of our developed Full Stage Learning to Rank solution, using both simulations and online A/B tests in one of the leading short-video recommendation platforms. The algorithm is proved to be effective in both retrieval and ranking stages. Since deployed, the algorithm has brought consistent and significant performance gain to the platform.

Recommendation systems aim to predict users' feedback on items not exposed to them yet. Confounding bias arises due to the presence of unmeasured variables (e.g., the socio-economic status of a user) that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. However, they cannot guarantee the identification of counterfactual feedback, which can lead to biased predictions. In this work, we propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables (e.g., observed user features) to resolve the aforementioned non-identification issue. The proposed iDCF is a general deconfounded recommendation framework that applies proximal causal inference to infer the unmeasured confounders and identify the counterfactual feedback with theoretical guarantees. Extensive experiments on various real-world and synthetic datasets verify the proposed method's effectiveness and robustness.

Contextual bandit algorithms have been popularly used to address interactive recommendation, where the users are assumed to be cooperative to explore all recommendations from a system. In this paper, we relax this strong assumption and study the problem of incentivized exploration with myopic users, where the users are only interested in recommendations with their currently highest estimated reward. As a result, in order to obtain long-term optimality, the system needs to offer compensation to incentivize the users to take the exploratory recommendations. We consider a new and practically motivated setting where the context features employed by the user are more informative than those used by the system: for example, features based on users’ private information are not accessible by the system. We develop an effective solution for incentivized exploration under such an information gap, and prove that the method achieves a sublinear rate in both regret and compensation. We theoretically and empirically analyze the added compensation due to the information gap, compared with the case where the system has access to the same context features as the user does, i.e., without information gap. Moreover, we also provide a compensation lower bound of this problem.

Advertisements (ads) are an innate part of search engine business models. To promote sales, advertisers are willing to pay search engines to promote their content to a prominent position in the search result page (SERP). This raises concerns about the search engine manipulation effect (SEME): the opinions of users can be influenced by the way search results are presented

Conversational recommender systems (CRS) explicitly solicit users' preferences for improved recommendations on the fly. Most existing CRS solutions count on a single policy trained by reinforcement learning for a population of users. However, for users new to the system, such a global policy becomes ineffective to satisfy them, i.e., the cold-start challenge. In this paper, we study CRS policy learning for cold-start users via meta reinforcement learning. We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations. To facilitate fast policy adaptation, we design three synergetic components. Firstly, we design a meta-exploration policy dedicated to identifying user preferences via a few exploratory conversations, which accelerates personalized policy adaptation from the meta policy. Secondly, we adapt the item recommendation module for each user to maximize the recommendation quality based on the collected conversation states during conversations. Thirdly, we propose a Transformer-based state encoder as the backbone to connect the previous two components. It provides comprehensive state representations by modeling complicated relations between positive and negative feedback during the conversation. Extensive experiments on three datasets demonstrate the advantage of our solution in serving new users, compared with a rich set of state-of-the-art CRS solutions.

Accuracy and diversity have long been considered to be two conflicting goals for recommendations. We point out, however, that as the diversity is typically measured by certain pre-selected item attributes, e.g., category as the most popularly employed one, improved diversity can be achieved without sacrificing recommendation accuracy, as long as the diversification respects the user's preference about the pre-selected attributes. This calls for a fine-grained understanding of a user's preferences over items, where one needs to recognize the user's choice is driven by the quality of the item itself, or the pre-selected attributes of the item.

Conversational recommender systems (CRS) dynamically obtain the users' preferences via multi-turn questions and answers. The existing CRS solutions are widely dominated by deep reinforcement learning algorithms. However, deep reinforcement learning methods are often criticized for lacking interpretability and requiring a large amount of training data to perform.

Graph Convolutional Networks (GCNs) have fueled a surge of research interest due to their encouraging performance on graph learning tasks, but they are also shown vulnerability to adversarial attacks. In this paper, an effective graph structural attack is investigated to disrupt graph spectral filters in the Fourier domain, which are the theoretical foundation of GCNs. We define the notion of spectral distance based on the eigenvalues of graph Laplacian to measure the disruption of spectral filters. We realize the attack by maximizing the spectral distance and propose an efficient approximation to reduce the time complexity brought by eigen-decomposition. The experiments demonstrate the remarkable effectiveness of the proposed attack in both black-box and white-box settings for both test-time evasion attacks and training-time poisoning attacks. Our qualitative analysis suggests the connection between the imposed spectral changes in the Fourier domain and the attack behavior in the spatial domain, which provides empirical evidence that maximizing spectral distance is an effective way to change the graph structural property and thus disturb the frequency components for graph filters to affect the learning of GCNs.

Bandit algorithms have become a reference solution for interactive recommendation. However, as such algorithms directly interact with users for improved recommendations, serious privacy concerns have been raised regarding its practical use. In this work, we propose a differentially private linear contextual bandit algorithm, via a tree-based mechanism to add Laplace or Gaussian noise to model parameters. Our key insight is that as the model converges during online update, the global sensitivity of its parameters shrinks over time (thus named dynamic global sensitivity). Compared with existing solutions, our dynamic global sensitivity analysis allows us to inject less noise to obtain (ϵ, δ)-differential privacy with added regret caused by noise injection in . We provide a rigorous theoretical analysis over the amount of noise added via dynamic global sensitivity and the corresponding upper regret bound of our proposed algorithm. Experimental results on both synthetic and real-world datasets confirmed the algorithm’s advantage against existing solutions.

Deep neural networks (DNNs) demonstrates significant advantages in improving ranking performance in retrieval tasks. Driven by the recent developments in optimization and generalization of DNNs, learning a neural ranking model online from its interactions with users becomes possible. However, the required exploration for model learning has to be performed in the entire neural network parameter space, which is prohibitively expensive and limits the application of such online solutions in practice.

This paper studies a remarkable property of graphs which is the latent hierarchical grouping of nodes, where each node manifests its membership to a specific group based on the context composed by its neighboring nodes. When modeling the neighborhood structure for graph representation learning, most prior works ignore such latent groups and nodes' membership to different groups, not to mention the hierarchy. Thus, they fall short of delivering a comprehensive understanding of the nodes under different contexts in a graph. In this paper, we propose a novel hierarchical attentive membership model for graph embedding, where the latent memberships for each node are dynamically discovered based on its neighboring context. Both group-level and individual-level attentions are performed when aggregating neighboring states to generate node embeddings. We introduce structural constraints to explicitly regularize the inferred memberships of each node, such that a well-defined hierarchical grouping structure is captured. The proposed model outperformed a set of state-of-the-art graph embedding solutions on node classification and link prediction tasks in a variety of graphs including citation networks and social networks. Qualitative evaluations visualize the learned node embeddings along with the inferred memberships, which proved the concept of membership hierarchy and enables explainable embedding learning in graphs.

Existing online learning to rank (OL2R) solutions are limited to linear models, which are incompetent to capture possible non-linear relations between queries and documents. In this work, to unleash the power of representation learning in OL2R, we propose to directly learn a neural ranking model from users’ implicit feedback (e.g., clicks) collected on the fly. We focus on RankNet and LambdaRank, due to their great empirical success and wide adoption in offline settings, and control the notorious explore-exploit trade-off based on the convergence analysis of neural networks using neural tangent kernel. Specifically, in each round of result serving, exploration is only performed on document pairs where the predicted rank order between the two documents is uncertain; otherwise, the ranker’s predicted order will be followed in result ranking. We prove that under standard assumptions our OL2R solution achieves a gap-dependent upper regret bound of O(log 2(T)), in which the regret is defined on the total number of mis-ordered pairs over T rounds. Comparisons against an extensive set of state-of-the-art OL2R baselines on two public learning to rank benchmark datasets demonstrate the effectiveness of the proposed solution.

Explanations in a recommender system assist users make informed decisions among a set of recommended items. Extensive research attention has been devoted to generate natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable, and personalized at the same time.

Graph embedding techniques are pivotal in real-world machine learning tasks that operate on graph-structured data, such as social recommendation and protein structure modeling. Embeddings are mostly performed on the node level for learning representations of each node. Since the formation of a graph is inevitably affected by certain sensitive node attributes, the node embeddings can inherit such sensitive information and introduce undesirable biases in downstream tasks. Most existing works impose ad-hoc constraints on the node embeddings to restrict their distributions for unbiasedness/fairness, which however compromise the utility of the resulting embeddings. In this paper, we propose a principled new way for unbiased graph embedding by learning node embeddings from an underlying bias-free graph, which is not influenced by sensitive node attributes. Motivated by this new perspective, we propose two complementary methods for uncovering such an underlying graph, with the goal of introducing minimum impact on the utility of the embeddings. Both our theoretical justification and extensive experimental comparisons against state-of-the-art solutions demonstrate the effectiveness of our proposed methods.

As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparative explanations about the recommended items. Ideally, after reading the explanations, a user should reach the same ranking of items as the system’s. Unfortunately, little research attention has yet been paid on such comparative explanations.

Crowdsourcing provides an efficient label collection schema for supervised machine learning. However, to control annotation cost, each instance in the crowdsourced data is typically annotated by a small number of annotators. This creates a sparsity issue and limits the quality of machine learning models trained on such data. In this paper, we study how to handle sparsity in crowdsourced data using data augmentation. Specifically, we propose to directly learn a classifier by augmenting the raw sparse annotations. We implement two principles of high-quality augmentation using Generative Adversarial Networks: 1) the generated annotations should follow the distribution of authentic ones, which is measured by a discriminator; 2) the generated annotations should have high mutual information with the ground-truth labels, which is measured by an auxiliary network. Extensive experiments and comparisons against an array of state-of-the-art learning from crowds methods on three real-world datasets proved the effectiveness of our data augmentation framework. It shows the potential of our algorithm for low-budget crowdsourcing in general.

Sequential recommendation is the task of predicting the next items for users based on their interaction history. Modeling the dependence of the next action on the past actions accurately is crucial to this problem. Moreover, sequential recommendation often faces serious sparsity of item-to-item transitions in a user's action sequence, which limits the practical utility of such solutions.

Search engines are perceived as a reliable source for general information needs. However, finding the answer to medical questions using search engines can be challenging for an ordinary user. Content can be biased and results may present different opinions. In addition, interpreting medically related content can be difficult for users with no medical background. All of these can lead users to incorrect conclusions regarding health related questions.

Collaborative bandit learning, i.e., bandit algorithms that utilize collaborative filtering techniques to improve sample efficiency in online interactive recommendation, has attracted much research attention as it enjoys the best of both worlds. However, all existing collaborative bandit learning solutions impose a stationary assumption about the environment, i.e., both user preferences and the dependency among users are assumed static over time. Unfortunately, this assumption hardly holds in practice due to users' ever-changing interests and dependency relations, which inevitably costs a recommender system sub-optimal performance in practice. In this work, we develop a collaborative dynamic bandit solution to handle a changing environment for recommendation. We explicitly model the underlying changes in both user preferences and their dependency relation as a stochastic process. Individual user's preference is modeled by a mixture of globally shared contextual bandit models with a Dirichlet process prior. Collaboration among users is thus achieved via Bayesian inference over the global bandit models. To balance exploitation and exploration during the interactions, Thompson sampling is used for both model selection and arm selection. Our solution is proved to maintain a standard ~O(√T) Bayesian regret in this challenging environment. Extensive empirical evaluations on both synthetic and real-world datasets further confirmed the necessity of modeling a changing environment and our algorithm's practical advantages against several state-of-the-art online learning solutions.

Information retrieval (IR) in nature is a process of sequential decision making. The system repeatedly interacts with the users to refine its understanding of the users' information needs, improve its estimation of result relevance, and thus increase the utility of its returned results (e.g., the result rankings). Distinct from traditional IR solutions that rigidly execute an offline trained policy, interactive information retrieval emphasizes online policy learning. This, however, is fundamentally difficult for at least three reasons. First, the system only collects user feedback on the presented results, aka, the bandit feedback. Second, users' feedback is known to be noisy and biased. Third, as a result, the system always faces the conflicting goals of improving its policy by presenting currently underestimated results to users versus satisfying the users by ranking the currently estimated best results on top. In this tutorial, we will first motivate the need for online policy learning in interactive IR, by highlighting its importance in several real-world IR problems where online sequential decision making is necessary, such as web search and recommendations. We will carefully address the new challenges that arose in such a solution paradigm, including sample complexity, costly and even outdated feedback, and ethical considerations in online learning (such as fairness and privacy) in interactive IR. We will prepare the technical discussions by first introducing several classical interactive learning strategies from machine learning literature, and then fully dive into the recent research developments for addressing the aforementioned fundamental challenges in interactive IR. Note that the tutorial on "Interactive Information Retrieval: Models, Algorithms, and Evaluation" will provide a broad overview on the general conceptual framework and formal models in interactive IR, while this tutorial covers the online policy learning solutions for interactive IR with bandit feedback.

Recent advances in unbiased learning to rank (LTR) count on Inverse Propensity Scoring (IPS) to eliminate bias in implicit feedback. Though theoretically sound in correcting the bias introduced by treating clicked documents as relevant, IPS ignores the bias caused by (implicitly) treating non-clicked ones as irrelevant. In this work, we first rigorously prove that such use of click data leads to unnecessary pairwise comparisons between relevant documents, which prevent unbiased ranker optimization.

Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanation with the learning of recommendation: for example, they are often separately modeled as rating prediction and content generation tasks. In this work, we propose to strengthen their connection by enforcing the idea of sentiment alignment between a recommendation and its corresponding explanation. At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation. At both training and inference time, the explanation module is required to generate explanation text that matches sentiment predicted by the recommendation module. Extensive experiments demonstrate our solution outperforms a rich set of baselines in both recommendation and explanation tasks, especially on the improved quality of its generated explanations. More importantly, our user studies confirm our generated explanations help users better recognize the differences between recommended items and understand why an item is recommended.

Online Learning to Rank (OL2R) eliminates the need of explicit relevance annotation by directly optimizing the rankers from their interactions with users. However, the required exploration drives it away from successful practices in offline learning to rank, which limits OL2R’s empirical performance and practical applicability. In this work, we propose to estimate a pairwise learning to rank model online. In each round, candidate documents are partitioned and ranked according to the model’s confidence on the estimated pairwise rank order, and exploration is only performed on the uncertain pairs of documents, i.e., divide-and-conquer. Regret directly defined on the number of mis-ordered pairs is proven, which connects the online solution’s theoretical convergence with its expected ranking performance. Comparisons against an extensive list of OL2R baselines on two public learning to rank benchmark datasets demonstrate the effectiveness of the proposed solution.

Edges play a crucial role in passing information on a graph, especially when they carry textual content reflecting semantics behind how nodes are linked and interacting with each other. In this paper, we propose a channel-aware attention mechanism enabled by edge text content when aggregating information from neighboring nodes; and we realize this mechanism in a graph autoencoder framework. Edge text content is encoded as low-dimensional mixtures of latent topics, which serve as semantic channels for topic-level information passing on edges. We embed nodes and topics in the same latent space to capture their mutual dependency when decoding the structural and textual information on graph. We evaluated the proposed model on Yelp user-item bipartite graph and StackOverflow user-user interaction graph. The proposed model outperformed a set of baselines on link prediction and content prediction tasks. Qualitative evaluations also demonstrated the descriptive power of the learnt node embeddings, showing its potential as an interpretable representation of graphs.

User-provided multi-aspect evaluations manifest users' detailed feedback on the recommended items and enable fine-grained understanding of their preferences. Extensive studies have shown that modeling such data greatly improves the effectiveness and explainability of the recommendations. However, as ranking is essential in recommendation, there is no principled solution yet for collectively generating multiple item rankings over different aspects.

Learning is a predominant theme for any intelligent system, humans, or machines. Moving beyond the classical paradigm of learning from past experience, e.g., offline supervised learning from given labels, a learner needs to actively collect exploratory feedback to learn from the unknowns, i.e., learning through exploration. This tutorial will introduce the learning by exploration paradigm, which is the key ingredient in many interactive online learning problems, including the multi-armed bandit and, more generally, reinforcement learning problems.

Collaborative bandit learning has become an emerging focus for personalized recommendation. It leverages user dependence for joint model estimation and recommendation. As such online learning solutions directly learn from users, e.g., result clicks, they bring in new challenges in privacy protection. Despite the existence of recent studies about privacy in contextual bandit algorithms, how to efficiently protect user privacy in a collaborative bandit learning environment remains unknown.

User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users' social connections and text content, to facilitate joint modeling of different modalities, via a probabilistic generative framework. We evaluated the proposed solution on large collections of Yelp reviews and StackOverflow discussion posts, with their associated network structures. The proposed model outperformed several state-of-the-art topic modeling based user models with better predictive power in unseen documents, and state-of-the-art network embedding based user models with improved link prediction quality in unseen nodes. The learnt user representations are also proved to be useful in content recommendation, e.g., expert finding in StackOverflow.

Predicting users’ preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems. Most existing sequential recommendation algorithms focus on transitional structure among the sequential actions, but largely ignore the temporal and context information, when modeling the influence of a historical event to current prediction.

Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with fixed number of sensors installed, when compared to the state-of-the-art, which is also proven by our theoretical analysis.

In this paper, we study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with the network, i.e., repeatedly selecting seed nodes and observing activation feedback in the network. We capitalize on an important property of the influence maximization problem named network assortativity, which is ignored by most existing works in online influence maximization. To realize network assortativity, we factorize the activation probability on the edges into latent factors on the corresponding nodes, including influence factor on the giving nodes and susceptibility factor on the receiving nodes. We propose an upper confidence bound based online learning solution to estimate the latent factors, and therefore the activation probabilities. Considerable regret reduction is achieved by our factorization based online influence maximization algorithm. Extensive empirical evaluations on two real-world networks showed the effectiveness of our proposed solution.

We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural network is introduced to learn search context representation of individual queries, search tasks, and corresponding dependency structure by jointly optimizing two companion retrieval tasks: document ranking and query suggestion. To identify variable dependency structure between search context and users' ongoing search activities, attention at both levels of recurrent states are introduced. Extensive experiment comparisons against a rich set of baseline methods and an in-depth ablation analysis confirm the value of our proposed approach for modeling search context buried in search tasks.

Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors. Specifically, we build regression trees on users and items respectively with user-generated reviews, and associate a latent profile to each node on the trees to represent users and items. With the growth of regression tree, the latent factors are gradually refined under the regularization imposed by the tree structure. As a result, we are able to track the creation of latent profiles by looking into the path of each factor on regression trees, which thus serves as an explanation for the resulting recommendations. Extensive experiments on two large collections of Amazon and Yelp reviews demonstrate the advantage of our model over several competitive baseline algorithms. Besides, our extensive user study also confirms the practical value of explainable recommendations generated by our model.

Online Learning to Rank (OL2R) algorithms learn from implicit user feedback on the fly. The key to such algorithms is an unbiased estimate of gradients, which is often (trivially) achieved by uniformly sampling from the entire parameter space. Unfortunately, this leads to high-variance in gradient estimation, resulting in high regret during model updates, especially when the dimension of the parameter space is large.

A user-generated review document is a product between the item's intrinsic properties and the user's perceived composition of those properties. Without properly modeling and decoupling these two factors, one can hardly obtain any accurate user understanding nor item profiling from such user-generated data. In this paper, we study a new text mining problem that aims at differentiating a user's subjective composition of topical content in his/her review document from the entity's intrinsic properties. Motivated by the Item Response Theory (IRT), we model each review document as a user's detailed response to an item, and assume the response is jointly determined by the individuality of the user and the property of the item. We model the text-based response with a generative topic model, in which we characterize the items' properties and users' manifestations of them in a low-dimensional topic space. Via posterior inference, we separate and study these two components over a collection of review documents. Extensive experiments on two large collections of Amazon and Yelp review data verified the effectiveness of the proposed solution: it outperforms the state-of-art topic models with better predictive power in unseen documents, which is directly translated into improved performance in item recommendation and item summarization tasks.

research-article Share on A Tree-Structured Neural Network Model for Household Energy Breakdown Authors: Yiling Jia University of Virginia, USA University of Virginia, USAView Profile , Nipun Batra IIT Gandhinagar, India IIT Gandhinagar, IndiaView Profile , Hongning Wang University of Virginia, USA University of Virginia, USAView Profile , Kamin Whitehouse University of Virginia, USA University of Virginia, USAView Profile Authors Info & Claims WWW '19: The World Wide Web ConferenceMay 2019 Pages 2872–2878https://doi.org/10.1145/3308558.3313405Published:13 May 2019Publication History 14citation306DownloadsMetricsTotal Citations14Total Downloads306Last 12 Months61Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.

Recommender systems have to handle a highly non-stationary environment, due to users' fast changing interests over time. Traditional solutions have to periodically rebuild their models, despite high computational cost. But this still cannot empower them to automatically adjust to abrupt changes in trends caused by timely information. It is important to note that the changes of reward distributions caused by a non-stationary environment can also be context dependent. When the change is orthogonal to the given context, previously maintained models should be reused for better recommendation prediction.

The massively available data about user engagement with online information service systems provides a gold mine about users' latent intents. It calls for quantitative user behavior modeling. In this paper, we study the problem by looking into users' sequential interactive behaviors. Inspired by the concepts of episodic memory and semantic memory in cognitive psychology, which describe how users' behaviors are differently influenced by past experience, we propose a Long- and Short-term Hawkes Process model. It models the short-term dependency between users' actions within a period of time via a multi-dimensional Hawkes process and the long-term dependency between actions across different periods of time via a one dimensional Hawkes process. Experiments on two real-world user activity log datasets (one from an e-commerce website and one from a MOOC website) demonstrate the effectiveness of our model in capturing the temporal dependency between actions in a sequence of user behaviors. It directly leads to improved accuracy in predicting the type and the time of the next action. Interestingly, the inferred dependency between actions in a sequence sheds light on the underlying user intent behind direct observations and provides insights for downstream applications.

User modeling is critical for understanding user intents, while it is also challenging as user intents are so diverse and not directly observable. Most existing works exploit specific types of behavior signals for user modeling, e.g., opinionated data or network structure; but the dependency among different types of user-generated data is neglected.

Part-of-speech (POS) tagging is the foundation of many natural language processing applications. Rule-based POS tagging is a wellknown solution, which assigns tags to the words using a set of predefined rules. Many researchers favor statistical-based approaches over rule-based methods for better empirical accuracy. However, until now, the computational cost of rule-based POS tagging has made it difficult to study whether more complex rules or larger rulesets could lead to accuracy competitive with statistical approaches. In this paper, we leverage two hardware accelerators, the Automata Processor (AP) and Field Programmable Gate Arrays (FPGA), to accelerate rule-based POS tagging by converting rules to regular expressions and exploiting the highly-parallel regular-expressionmatching ability of these accelerators. We study the relationship between rule set size and accuracy, and observe that adding more rules only poses minimal overhead on the AP and FPGA. This allows a substantial increase in the number and complexity of rules, leading to accuracy improvement. Our experiments on Treebank and Brown corpora achieve up to 2,600X and 1,914X speedups on the AP and on the FPGA respectively over rule-based methods on the CPU in the rule-matching stage, up to 58× speedup over the Perceptron POS tagger on the CPU in total testing time, and up to 253× speedup over the LSTM tagger on the GPU in total testing time, while showing a competitive accuracy compared to neural-network and statistical solutions.

Modern web search engines exploit users' search history to personalize search results, with a goal of improving their service utility on a per-user basis. But it is this very dimension that leads to the risk of privacy infringement and raises serious public concerns. In this work, we propose a client-centered intent-aware query obfuscation solution for protecting user privacy in a personalized web search scenario. In our solution, each user query is submitted with l additional cover queries and corresponding clicks, which act as decoys to mask users' genuine search intent from a search engine. The cover queries are sequentially sampled from a set of hierarchically organized language models to ensure the coherency of fake search intents in a cover search task. Our approach emphasizes the plausibility of generated cover queries, not only to the current genuine query but also to previous queries in the same task, to increase the complexity for a search engine to identify a user's true intent. We also develop two new metrics from an information theoretic perspective to evaluate the effectiveness of provided privacy protection. Comprehensive experiment comparisons with state-of-the-art query obfuscation techniques are performed on the public AOL search log, and the propitious results substantiate the effectiveness of our solution.

Online learning to rank (OL2R) optimizes the utility of returned search results based on implicit feedback gathered directly from users. In this paper, we accelerate the online learning process by efficient exploration in the gradient space. Our algorithm, named as Null Space Gradient Descent, reduces the exploration space to only the null space of recent poorly performing gradients. This prevents the algorithm from repeatedly exploring directions that have been discouraged by the most recent interactions with users. To improve sensitivity of the resulting interleaved test, we selectively construct candidate rankers to maximize the chance that they can be differentiated by candidate ranking documents in the current query; and we use historically difficult queries to identify the best ranker when tie occurs in comparing the rankers. Extensive experimental comparisons with the state-of-the-art OL2R algorithms on several public benchmarks confirmed the effectiveness of our proposal algorithm, especially in its fast learning convergence and promising ranking quality at an early stage.

Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. Two companion learning tasks of user preference modeling for recommendation and opinionated content modeling for explanation are integrated via a joint tensor factorization. As a result, the algorithm predicts not only a user's preference over a list of items, i.e., recommendation, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation. Extensive experiments on two large collections of Amazon and Yelp reviews confirmed the effectiveness of our solution in both recommendation and explanation tasks, compared with several existing recommendation algorithms. And our extensive user study clearly demonstrates the practical value of the explainable recommendations generated by our algorithm.

Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such as display advertisement. However, such algorithms usually assume a stationary reward distribution, which hardly holds in practice as users' preferences are dynamic. This inevitably costs a recommender system consistent suboptimal performance. In this paper, we consider the situation where the underlying distribution of reward remains unchanged over (possibly short) epochs and shifts at unknown time instants. In accordance, we propose a contextual bandit algorithm that detects possible changes of environment based on its reward estimation confidence and updates its arm selection strategy respectively. Rigorous upper regret bound analysis of the proposed algorithm demonstrates its learning effectiveness in such a non-trivial environment. Extensive empirical evaluations on both synthetic and real-world datasets for recommendation confirm its practical utility in a changing environment.

We develop Hide-n-Seek, an intent-aware privacy protection plugin for personalized web search. In addition to users' genuine search queries, Hide-n-Seek submits k cover queries and corresponding clicks to an external search engine to disguise a user's search intent grounded and reinforced in a search session by mimicking the true query sequence. The cover queries are synthesized and randomly sampled from a topic hierarchy, where each node represents a coherent search topic estimated by both n-gram and neural language models constructed over crawled web documents. Hide-n-Seek also personalizes the returned search results by re-ranking them based on the genuine user profile developed and maintained on the client side. With a variety of graphical user interfaces, we present the topic-based query obfuscation mechanism to the end users for them to digest how their search privacy is protected.

The recorded student activities in Massive Open Online Course (MOOC) provide us a unique opportunity to model their learning behaviors, identify their particular learning intents, and enable personalized assistance and guidance in online education. In this work, based on a thorough qualitative study of students' behaviors recorded in two MOOC courses with large student enrollments, we develop a non-parametric Bayesian model to capture students' sequential learning activities in a generative manner. Homogeneity of students' learning behaviors is captured by clustering them into latent student groups, where shared model structure characterizes the transitional patterns, intensity and temporal distribution of their learning activities. In the meanwhile, heterogeneity is captured by clustering students into different groups. Both qualitative and quantitative studies on those two MOOC courses confirmed the effectiveness of the proposed model in identifying students' learning behavior patterns and clustering them into related groups for predictive analysis. The identified student groups accurately predict student retention, course satisfaction and demographics.

In this work, we propose to improve long-term user engagement in a recommender system from the perspective of sequential decision optimization, where users' click and return behaviors are directly modeled for online optimization. A bandit-based solution is formulated to balance three competing factors during online learning, including exploitation for immediate click, exploitation for expected future clicks, and exploration of unknowns for model estimation. We rigorously prove that with a high probability our proposed solution achieves a sublinear upper regret bound in maximizing cumulative clicks from a population of users in a given period of time, while a linear regret is inevitable if a user's temporal return behavior is not considered when making the recommendations. Extensive experimentation on both simulations and a large-scale real-world dataset collected from Yahoo frontpage news recommendation log verified the effectiveness and significant improvement of our proposed algorithm compared with several state-of-the-art online learning baselines for recommendation.

One important way for people to make their voice heard is to comment on the articles they have read online, such as news reports and each other's posts. The user-generated comments together with the commented documents form a unique correspondence structure. Properly modeling the dependency in such data is thus vital for one to obtain accurate insight of people's opinions and attention.

We develop an aspect-based sentiment analysis system named ReviewMiner. It analyzes opinions expressed about an entity in an online review at the level of topical aspects to discover each individual reviewer's latent opinion on each aspect as well as his/her relative emphasis on different aspects when forming the overall judgment of the entity. The system personalizes the retrieved results according to users' input preferences over the identified aspects, recommends similar items based on the detailed aspect-level opinions, and summarizes aspect-level opinions in textual, temporal and spatial dimensions. The unique multi-modal opinion summarization and visualization mechanisms provide users with rich perspectives to digest information from user-generated opinionated content for making informed decisions.

We propose to capture humans' variable and idiosyncratic sentiment via building personalized sentiment classification models at a group level. Our solution roots in the social comparison theory that humans tend to form groups with others of similar minds and ability, and the cognitive consistency theory that mutual influence inside groups will eventually shape group norms and attitudes, with which group members will all shift to align. We formalize personalized sentiment classification as a multi-task learning problem. In particular, to exploit the clustering property of users' opinions, we impose a non-parametric Dirichlet Process prior over the personalized models, in which group members share the same customized sentiment model adapted from a global classifier. Extensive experimental evaluations on large collections of Amazon and Yelp reviews confirm the effectiveness of the proposed solution: it outperformed user-independent classification solutions, and several state-of-the-art model adaptation and multi-task learning algorithms.

In recent years, mobile devices have become the most popular interface for users to retrieve and access information: recent reports show that users spend significantly more time and issue more search queries on mobile devices than on desktops in the United States. 1 The accelerated growth of mobile usage brings unique opportunities to the information retrieval and data mining research communities. Mobile devices capture rich contextual and personal signals that can be leveraged to accurately predict users’ intent for serving more relevant content and can even proactively provide novel zero-query recommendations. Apple Siri, Google Now, and Microsoft Cortana are recent examples of such emerging systems. Furthermore, mobile devices constantly generate a huge amount of sensor footprints (e.g., GPS, motion sensors) and user activity data (e.g., used apps) that are often missing from their desktop counterparts. These new sources of implicit and explicit user feedback are valuable for discovering actionable knowledge, and designing better systems that serve each individual the right content at the right time and location. In addition, by aggregating mobile interactions across individuals, one can infer interesting conclusions beyond search and recommendation. Generating real-time traffic estimates is one example of such applications. This special issue focuses on research problems of search, mining, and their applications in mobile devices. Topics of interest in this special issue include but are not limited to mobile data mining and management, mobile search, personalization and recommendation, mobile user interfaces and human-computer interaction, and new applications in the mobile environment. The aim of this special issue is to bring together top experts across multiple disciplines, including information retrieval, data mining, mobile computing, and cyberphysical systems, such that academic and industrial researchers can exchange ideas and share the latest developments on the state of the art and practice of mobile search and mobile data mining.

Contextual bandit algorithms provide principled online learning solutions to find optimal trade-offs between exploration and exploitation with companion side-information. Most contextual bandit algorithms simply assume the learner would have access to the entire set of features, which govern the generation of payoffs from a user to an item. However, in practice it is challenging to exhaust all relevant features ahead of time, and oftentimes due to privacy or sampling constraints many factors are unobservable to the algorithm. Failing to model such hidden factors leads a system to make constantly suboptimal predictions. In this paper, we propose to learn the hidden features for contextual bandit algorithms. Hidden features are explicitly introduced in our reward generation assumption, in addition to the observable contextual features. A scalable bandit algorithm is achieved via coordinate descent, in which closed form solutions exist at each iteration for both hidden features and bandit parameters. Most importantly, we rigorously prove that the developed contextual bandit algorithm achieves a sublinear upper regret bound with high probability, and a linear regret is inevitable if one fails to model such hidden features. Extensive experimentation on both simulations and large-scale real-world datasets verified the advantages of the proposed algorithm compared with several state-of-the-art contextual bandit algorithms and existing ad-hoc combinations between bandit algorithms and matrix factorization methods.

Modern search engines utilize users' search history for personalization, which provides more effective, useful and relevant search results. However, it also has the potential risk of revealing users' privacy by identifying their underlying intention from their logged search behaviors. To address this privacy issue, we proposed a Topic-based Privacy Protection solution on client side. In our solution, each user query will be submitted with k additional cover queries, which will act as a proxy to disguise users' intent from a search engine. The set of cover queries are generated in a controlled way so that each query carries similar uncertainty to randomize a user's search history while still providing necessary utility for the search engine to perform personalization. We used statistical topic models to infer topics from the original user query and generated cover queries of similar entropy but from unrelated topics. Extensive experiments are performed on AOL search log and the promising results demonstrated the effectiveness of our solution.

Contextual bandit algorithms provide principled online learning solutions to find optimal trade-offs between exploration and exploitation with companion side-information. They have been extensively used in many important practical scenarios, such as display advertising and content recommendation. A common practice estimates the unknown bandit parameters pertaining to each user independently. This unfortunately ignores dependency among users and thus leads to suboptimal solutions, especially for the applications that have strong social components.

Various topic models have been developed for sentiment analysis tasks. But the simple topic-sentiment mixture assumption prohibits them from finding fine-grained dependency between topical aspects and sentiments. In this paper, we build a Hidden Topic Sentiment Model (HTSM) to explicitly capture topic coherence and sentiment consistency in an opinionated text document to accurately extract latent aspects and corresponding sentiment polarities. In HTSM, 1) topic coherence is achieved by enforcing words in the same sentence to share the same topic assignment and modeling topic transition between successive sentences; 2) sentiment consistency is imposed by constraining topic transitions via tracking sentiment changes; and 3) both topic transition and sentiment transition are guided by a parameterized logistic function based on the linguistic signals directly observable in a document. Extensive experiments on four categories of product reviews from both Amazon and NewEgg validate the effectiveness of the proposed model.

Commercial and industrial buildings account for a considerable portion of all energy consumed in the U.S., and thus reducing this energy consumption is a national grand challenge. Based on the large deployment of sensors in modern commercial buildings, many organizations are applying data analytic solutions to the thousands of sensing and control points to detect wasteful and incorrect operations for energy savings. Scaling this approach is challenging, however, because the metadata about these sensing and control points is inconsistent between buildings, or even missing altogether. Moreover, normalizing the metadata requires significant integration effort.

Query auto-completion (QAC) facilitates faster user query input by predicting users' intended queries. Most QAC algorithms take a learning-based approach to incorporate various signals for query relevance prediction. However, such models are trained on simulat- ed user inputs from query log data. The lack of real user interaction data in the QAC process prevents them from further improving the QAC performance. In this work, for the first time we collect a high-resolution QAC query log that records every keystroke in a QAC session. Based on this data, we discover two user behaviors, namely the horizontal skipping bias and vertical position bias which are crucial for rele- vance prediction in QAC. In order to better explain them, we pro- pose a novel two-dimensional click model for modeling the QAC process with emphasis on these behaviors. Extensive experiments on our QAC data set from both PC and mobile devices demonstrate that our proposed model can accurate- ly explain the users' behaviors in interacting with a QAC system, and the resulting relevance model significant improves the QAC performance over existing click models. Furthermore, the learned knowledge about the skipping behavior can be effectively incorpo- rated into existing learning-based models to further improve their performance.

Search satisfaction is a property of a user's search process. Understanding it is critical for search providers to evaluate the performance and improve the effectiveness of search engines. Existing methods model search satisfaction holistically at the search-task level, ignoring important dependencies between action-level satisfaction and overall task satisfaction. We hypothesize that searchers' latent action-level satisfaction (i.e., whether they believe they were satisfied with the results of a query or click) influences their observed search behaviors and contributes to overall search satisfaction. We conjecture that by modeling search satisfaction at the action level, we can build more complete and more accurate predictors of search-task satisfaction. To do this, we develop a latent structural learning method, whereby rich structured features and dependency relations unique to search satisfaction prediction are explored. Using in-situ search satisfaction judgments provided by searchers, we show that there is significant value in modeling action-level satisfaction in search-task satisfaction prediction. In addition, experimental results on large-scale logs from Bing.com demonstrate clear benefit from using inferred action satisfaction labels for other applications such as document relevance estimation and query suggestion.

RankNet is one of the widely adopted ranking models for web search tasks. However, adapting a generic RankNet for personalized search is little studied. In this paper, we first continue-trained a variety of RankNets with different number of hidden layers and network structures over a previously trained global RankNet model, and observed that a deep neural network with five hidden layers gives the best performance. To further improve the performance of adaptation, we propose a set of novel methods categorized into two groups. In the first group, three methods are proposed to properly assess the usefulness of each adaptation instance and only leverage the most informative instances to adapt a user-specific RankNet model. These assessments are based on KL-divergence, click entropy or a heuristic to ignore top clicks in adaptation queries. In the second group, two methods are proposed to regularize the training of the neural network in RankNet: one of these methods regularize the error back-propagation via a truncated gradient approach, while the other method limits the depth of the back propagation when adapting the neural network. We empirically evaluate our approaches using a large-scale real-world data set. Experimental results exhibit that our methods all give significant improvements over a strong baseline ranking system, and the truncated gradient approach gives the best performance, significantly better than all others.

Searchers' information needs are diverse and cover a broad range of topics; hence, it is important for search engines to accurately understand each individual user's search intents in order to provide optimal search results. Search log data, which records users' search behaviors when interacting with search engines, provides a valuable source of information about users' search intents. Therefore, properly characterizing the heterogeneity among the users' observed search behaviors is the key to accurately understanding their search intents and to further predicting their behaviors.

In this paper, we study the task of relational entity search which aims at automatically learning an entity ranking function for a desired relation. To rank entities, we exploit the redundancy abound in their snippets; however, such redundancy is noisy as not all the snippets represent information relevant to the desired relation. To explore useful information from such noisy redundancy, we abstract the task as a distantly supervised ranking problem — based on coarse entity-level annotations, deriving a relation-specific ranking function for the purpose of online searching. As the key challenge, without detailed snippet-level annotations, we have to learn an entity ranking function that can effectively filter noise; furthermore, the ranking function should also be online executable. We develop Pattern-based Filter Network (PFNet), a novel probabilistic graphical model, as our solution. To balance the accuracy and efficiency requirements, PFNet selects a limited size of indicative patterns to filter noisy snippets, and inverted indexes are utilized to retrieve required features. Experiments on the large scale CuleWeb09 data set for six different relations confirm the effectiveness of the proposed PFNet model, which outperforms five state-of-the-art relational entity ranking methods.

In typical studies of node grouping detection, the grouping is presumed to have a certain type of correlation with the network structure (e.g., densely connected groups of nodes that are loosely connected in between). People have defined different fitness measures (modularity, conductance, etc.) to quantify such correlation, and group the nodes by optimizing a certain fitness measure. However, a particular grouping with desired semantics, as the target of the detection, is not promised to be detectable by each measure. We study a fundamental problem in the process of node grouping discovery: Given a particular grouping in a network, whether and to what extent it can be discovered with a given fitness measure. We propose two approaches of testing the detectability, namely ranking-based and correlation-based randomization tests. Our methods are evaluated on both synthetic and real datasets, which shows the proposed methods can effectively predict the detectability of groupings of various types, and support explorative process of node grouping discovery.

With the development of social media and social networks, user-generated content, like forums, blogs and comments, are not only getting richer, but also ubiquitously interconnected with many other objects and entities, forming a heterogeneous information network between them. Sentiment analysis on such kinds of data can no longer ignore the information network, since it carries a lot of rich and valuable information, explicitly or implicitly, where some of them can be observed while others are not. In this paper, we propose a novel information network-based framework which can infer hidden similarity and dissimilarity between users by exploring similar and opposite opinions, so as to improve post-level and user-level sentiment classification in the same time. More specifically, we develop a new meta path-based measure for inferring pseudo-friendship as well as dissimilarity between users, and propose a semi-supervised refining model by encoding similarity and dissimilarity from both user-level and post-level relations. We extensively evaluate the proposed approach and compare with several state-of-the-art techniques on two real-world forum datasets. Experimental results show that our proposed model with 10.5% labeled samples can achieve better performance than a traditional supervised model trained on 61.7% data samples.

Search engines train and apply a single ranking model across all users, but searchers' information needs are diverse and cover a broad range of topics. Hence, a single user-independent ranking model is insufficient to satisfy different users' result preferences. Conventional personalization methods learn separate models of user interests and use those to re-rank the results from the generic model. Those methods require significant user history information to learn user preferences, have low coverage in the case of memory-based methods that learn direct associations between query-URL pairs, and have limited opportunity to markedly affect the ranking given that they only re-order top-ranked items.

In this paper, we present a log-based study on user search behavior comparisons on three different platforms: desktop, mobile and tablet. We use three-month search logs in 2012 from a commercial search engine for our study. Our objective is to better understand how and to what extent mobile and tablet searchers behave differently than desktop users. Our study spans a variety of aspects including query categorization, query length, search time distribution, search location distribution, user click patterns and so on. From our data set, we reveal that there are significant differences between user search patterns in these three platforms, and therefore use the same ranking system is not an optimal solution for all of them. Consequently, we propose a framework that leverages a set of domain-specific features, along with the training data from desktop search, to further improve the search relevance for mobile and tablet platforms. Experimental results demonstrate that by transferring knowledge from desktop search, search relevance on mobile and tablet can be greatly improved.

Search tasks, comprising a series of search queries serving the same information need, have recently been recognized as an accurate atomic unit for modeling user search intent. Most prior research in this area has focused on short-term search tasks within a single search session, and heavily depend on human annotations for supervised classification model learning. In this work, we target the identification of long-term, or cross-session, search tasks (transcending session boundaries) by investigating inter-query dependencies learned from users' searching behaviors. A semi-supervised clustering model is proposed based on the latent structural SVM framework, and a set of effective automatic annotation rules are proposed as weak supervision to release the burden of manual annotation. Experimental results based on a large-scale search log collected from Bing.com confirms the effectiveness of the proposed model in identifying cross-session search tasks and the utility of the introduced weak supervision signals. Our learned model enables a more comprehensive understanding of users' search behaviors via search logs and facilitates the development of dedicated search-engine support for long-term tasks.

Click models aim at extracting intrinsic relevance of documents to queries from biased user clicks. One basic modeling assumption made in existing work is to treat such intrinsic relevance as an atomic query-document-specific parameter, which is solely estimated from historical clicks without using any content information about a document or relationship among the clicked/skipped documents under the same query. Due to this overly simplified assumption, existing click models can neither fully explore the information about a document's relevance quality nor make predictions of relevance for any unseen documents.

Personalized search systems tailor search results to the current user intent using historic search interactions. This relies on being able to find pertinent information in that user's search history, which can be challenging for unseen queries and for new search scenarios. Building richer models of users' current and historic search tasks can help improve the likelihood of finding relevant content and enhance the relevance and coverage of personalization methods. The task-based approach can be applied to the current user's search history, or as we focus on here, all users' search histories as so-called "groupization" (a variant of personalization whereby other users' profiles can be used to personalize the search experience). We describe a method whereby we mine historic search-engine logs to find other users performing similar tasks to the current user and leverage their on-task behavior to identify Web pages to promote in the current ranking. We investigate the effectiveness of this approach versus query-based matching and finding related historic activity from the current user (i.e., group versus individual). As part of our studies we also explore the use of the on-task behavior of particular user cohorts, such as people who are expert in the topic currently being searched, rather than all other users. Our approach yields promising gains in retrieval performance, and has direct implications for improving personalization in search systems.

With more and more people freely express opinions as well as actively interact with each other in discussion threads, online forums are becoming a gold mine with rich information about people's opinions and social behaviors. In this paper, we study an interesting new problem of automatically discovering opposing opinion networks of users from forum discussions, which are subset of users who are strongly against each other on some topic. Toward this goal, we propose to use signals from both textual content (e.g., who says what) and social interactions (e.g., who talks to whom) which are both abundant in online forums. We also design an optimization formulation to combine all the signals in an unsupervised way. We created a data set by manually annotating forum data on five controversial topics and our experimental results show that the proposed optimization method outperforms several baselines and existing approaches, demonstrating the power of combining both text analysis and social network analysis in analyzing and generating the opposing opinion networks.

In contrast to traditional Web search, where topical relevance is often the main selection criterion, news search is characterized by the increased importance of freshness. However, the estimation of relevance and freshness, and especially the relative importance of these two aspects, are highly specific to the query and the time when the query was issued. In this work, we propose a unified framework for modeling the topical relevance and freshness, as well as their relative importance, based on click logs. We use click statistics and content analysis techniques to define a set of temporal features, which predict the right mix of freshness and relevance for a given query. Experimental results on both historical click data and editorial judgments demonstrate the effectiveness of the proposed approach.

Mining detailed opinions buried in the vast amount of review text data is an important, yet quite challenging task with widespread applications in multiple domains. Latent Aspect Rating Analysis (LARA) refers to the task of inferring both opinion ratings on topical aspects (e.g., location, service of a hotel) and the relative weights reviewers have placed on each aspect based on review content and the associated overall ratings. A major limitation of previous work on LARA is the assumption of pre-specified aspects by keywords. However, the aspect information is not always available, and it may be difficult to pre-define appropriate aspects without a good knowledge about what aspects are actually commented on in the reviews.

Online forum discussions are emerging as valuable information repository, where knowledge is accumulated by the interaction among users, leading to multiple threads with structures. Such replying structure in each thread conveys important information about the discussion content. Unfortunately, not all the online forum sites would explicitly record such replying relationship, making it hard to for both users and computers to digest the information buried in a thread discussion.

In this paper, we define and study a new opinionated text data analysis problem called Latent Aspect Rating Analysis (LARA), which aims at analyzing opinions expressed about an entity in an online review at the level of topical aspects to discover each individual reviewer's latent opinion on each aspect as well as the relative emphasis on different aspects when forming the overall judgment of the entity. We propose a novel probabilistic rating regression model to solve this new text mining problem in a general way. Empirical experiments on a hotel review data set show that the proposed latent rating regression model can effectively solve the problem of LARA, and that the detailed analysis of opinions at the level of topical aspects enabled by the proposed model can support a wide range of application tasks, such as aspect opinion summarization, entity ranking based on aspect ratings, and analysis of reviewers rating behavior.

Traditional discriminative classification method makes little attempt to reveal the probabilistic structure and the correlation within both input and output spaces. In the scenario of multi-label classification, most of the classifiers simply assume the predefined classes are independently distributed, which would definitely hinder the classification performance when there are intrinsic correlations between the classes. In this article, we propose a generative probabilistic model, the Correlated Labeling Model (CoL Model), to formulate the correlation between different classes. The CoL model is presented to capture the correlation between classes and the underlying structures via the latent random variables in a supervised manner. We develop a variational procedure to approximate the posterior distribution and employ the EM algorithm for the empirical Bayes parameter estimation. In our evaluations, the proposed model achieved promising results on various data sets.