Hateful videos have become prevalent on online platforms, highlighting an urgent need for effective detection. However, existing studies primarily focus on binary classification and fail to provide contextual rationales that reveal the implicit meanings behind these judgments, significantly undermining model explainability. To fill this gap, we aim to achieve explainable hateful video detection, enabling models to provide contextual rationales that integrate relevant evidence and logical reasoning alongside decisions. This approach can comprehensively enhance the understanding of video content and the explainability of the decision-making process. We first introduce two datasets, Ex-HateMM and Ex-ImpliHateVid, for explainable hateful video detection. Each dataset provides fine-grained annotations of multimodal harmful elements, along with contextual rationales. We then propose an Information Augmentation and Reasoning Enhancement (IARE) framework designed for explainable detection. The framework employs an information augmentation phase that leverages the multimodal chain-of-thought to integrate harmful elements, thereby enriching rationale evidence. Additionally, IARE incorporates a reasoning enhancement phase, in which Direct Preference Optimization guides the model toward correct reasoning paths and away from incorrect ones, thereby improving the logical coherence of its justifications. We conduct extensive experiments on the two datasets, comparing multiple baselines with our proposed IARE framework. The results demonstrate that IARE achieves state-of-the-art performance while also generating accurate rationales.

Metaphors are a fundamental cognitive tool for articulating abstract and subjective experiences and implicit semantics, making them potent indicators of psychological state, particularly in individuals with depression. The proliferation of social media has created a vast repository of such metaphorical expressions, offering an unprecedented opportunity to understand mental health struggles. These metaphors can provide crucial insights for clinical assessment and therapeutic intervention. However, their potential remains largely untapped in automated depression detection, primarily due to the lack of large-scale, annotated datasets. To bridge this gap, we introduce the Depression-Related Metaphor Dataset (DRMD), a novel resource of social media posts related to depression, incorporating depression levels (severe, moderate, minimum, and null), the presence or absence of metaphors, and their conceptual source domain mappings. We leverage this dataset to fine-tune Large Language Models (LLMs), integrating metaphorical features to enhance detection capabilities. Our results demonstrate that models incorporating metaphorical information achieve superior accuracy in depression detection and, importantly, generate high-quality explanations for their decisions by referencing specific metaphorical expressions. This work underscores the critical role of metaphorical analysis in computational mental health and provides a foundation for future research in explainable AI for depression detection. The dataset is publicly available.

Generative retrieval enhances retrieval effectiveness by generating natural language represented document identifiers. However, current methods often struggle with two major challenges: limited identifier quality and insufficient query-document interaction, leading to limited retrieval performance. To tackle these challenges, we propose a novel generative retrieval framework integrated with semantic-aligned multi-layer identifiers and reinforcement learning. To improve identifier quality, we design a prompt-driven multi-task learning strategy to generate three types of hierarchical identifiers: summary, keyword, and pseudo-query, to capture multi-granularity document semantics. Furthermore, we adopt supervised fine-tuning to integrate these identifiers. To improve query-document interaction, we devise a multi-view ranking fusion mechanism that combines retrieval results across multi-layer identifiers. We further employ a GRPO-based reinforcement learning based on dense similarity rewards and a difficulty-aware negative sampling strategy to optimize the generated identifiers. Experiments on multiple benchmark datasets show that our framework significantly outperforms existing generative retrieval methods, offering a promising solution for building more effective and semantically aligned retrieval systems. The code for our model is publicly available at https://github.com/yicentian02/GRAM-RL.

News recommender systems aim to provide personalized news reading experiences for users based on their reading history. Behavioral science studies suggest that screen-based news reading contains three successive steps: scanning, title reading, and then clicking. Adhering to these steps, we find that intra-news entity interest dominates the scanning stage, while the inter-news entity interest guides title reading and influences click decisions. Unfortunately, current methods overlook the unique utility of entities in news recommendation. To this end, we propose a novel method called IP2 to probe entity-guided reading interest at both intra- and inter-news levels. At the intra-news level, a Transformer-based entity encoder is devised to aggregate mentioned entities in the news title into one signature entity. Then, a signature entity-title contrastive pre-training is adopted to initialize entities with proper meanings using the news story context, which in the meantime facilitates us to probe for intra-news entity interest. As for the inter-news level, a dual tower user encoder is presented to capture inter-news reading interest from both the title meaning and entity sides. In addition to highlighting the contribution of inter-news entity guidance, a cross-tower attention link is adopted to calibrate title reading interest using inter-news entity interest, thus further aligning with real-world behavior. Extensive experiments on two real-world datasets demonstrate that our IP2 achieves state-of-the-art performance in news recommendation.

Hateful memes are prevalent on the Internet, raising the urgent need for effective detection.Given their implicit nature, incorporating rationales with background knowledge is crucial for enhancing model understanding.However, existing methods often suffer from limited quality of external rationales and misalignment with original meme information.These challenges hinder model comprehension, leading to reduced accuracy and explainability.To address these challenges, we propose a Multimodal Multi-agent Knowledge Enhanced (M2KE) framework for hateful meme detection.M2KE introduces a multi-agent rationale discovery mechanism to extract high-quality rationales relevant to meme content and an adaptive knowledge interaction mechanism to ensure alignment between original meme information and external rationales.Specifically, multi-agent rationale discovery mechanism improves the reliability of rationales by collaboratively verifying and refining them with multiple agents, supported by large language models (LLMs) due to their extensive knowledge.And adaptive knowledge interaction mechanism uses information entropy to dynamically balance the model's attention between original meme information and external rationales, preventing over-reliance on rationales and enabling a more comprehensive understanding.Experimental results on three datasets demonstrate that M2KE significantly outperforms existing models.Further analysis underscores the importance of effectively integrating accurate rationales to enhance model performance.Disclaimer: Samples in this paper may be considered offensive.

Session-based recommendation is gaining increasing attention due to its practical value in predicting the intents of anonymous users based on limited behaviors. Emerging efforts incorporate various side information to alleviate inherent data scarcity issues in this task, leading to impressive performance improvements. The core of side information-driven session-based recommendation is the discovery and utilization of diverse data. In this survey, we provide a comprehensive review of this task from a data-centric perspective. Specifically, this survey commences with a clear formulation of the task. This is followed by a detailed exploration of various benchmarks rich in side information that are pivotal for advancing research in this field. Afterwards, we delve into how different types of side information enhance the task, underscoring data characteristics and utility. Moreover, we discuss the usage of various side information, including data encoding, data injection, and involved techniques. A systematic review of research progress is then presented, with the taxonomy by the types of side information. Finally, we summarize the current limitations and present the future prospects of this vibrant topic.

Malevolence detection in dialogues aims to identify harmful or inappropriate utterances, significantly impacting dialogue quality and user satisfaction. Although existing studies have shown promising performance by modeling interaction patterns from dialogue history, various malevolence-invoking factors, such as fine-grained emotions, evolving topics and user profiles, are often overlooked. To comprehensively consider these factors, we propose a hypergraph fusion model by employing multi-view LLM-driven prompts for malevolence detection in dialogues. Our model integrates emotion context, topic context, user profile context and interaction context, utilizing hypergraphs to establish high-order contextual relationships from multi views for deducing malevolence-invoking semantics. Experimental results on two benchmark datasets demonstrate that our model achieves the state-of-the-art performance.

Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence patterns reflected by item IDs, and fine-grained preferences represented by item modalities (e.g., text and images). However, existing methods typically entangle these causes, leading to their failure in achieving accurate and explainable recommendations. To this end, we propose a novel framework DIMO to disentangle the effects of ID and modality in the task. DIMO aims to disentangle these causes at both item and session levels. At the item level, we introduce a co-occurrence representation schema to explicitly incorporate co-occurrence patterns into ID representations. Simultaneously, DIMO aligns different modalities into a unified semantic space to represent them uniformly. At the session level, we present a multi-view self-supervised disentanglement, including proxy mechanism and counterfactual inference, to disentangle ID and modality effects without supervised signals. Leveraging these disentangled causes, DIMO provides recommendations via causal inference and further creates two templates for generating explanations. Extensive experiments on multiple real-world datasets demonstrate the consistent superiority of DIMO over existing methods. Further analysis also confirms DIMO's effectiveness in generating explanations.

Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the attribute-opinion pairs of reviews to finely handle sequential recommendation. Specifically, we utilize a large language model to extract attribute-opinion pairs from reviews. For each attribute, a unique attribute-specific user-opinion-item graph is created, where corresponding opinions serve as the edges linking heterogeneous user and item nodes. Afterwards, we devise a diversity-aware convolution operation to aggregate information within the graphs, enabling attribute-specific user and item representation learning. Ultimately, we present an interaction-driven fusion mechanism to integrate attribute-specific user/item representations across all attributes for generating recommendations. Extensive experiments conducted on several real-world datasets demonstrate the superiority of our FineRec over existing state-ofthe-art methods. Further analysis also verifies the effectiveness of our fine-grained manner in handling the task.

Session-based recommendation is devoted to characterizing preferences of anonymous users based on short sessions. Existing methods mostly focus on mining limited item co-occurrence patterns exposed by item ID within sessions, while ignoring what attracts users to engage with certain items is rich multi-modal information displayed on pages. Generally, the multi-modal information can be classified into two categories: descriptive information (e.g., item images and description text) and numerical information (e.g., price). In this paper, we aim to improve session-based recommendation by modeling the above multi-modal information holistically. There are mainly three issues to reveal user intent from multi-modal information: (1) How to extract relevant semantics from heterogeneous descriptive information with different noise? (2) How to fuse these heterogeneous descriptive information to comprehensively infer user interests? (3) How to handle probabilistic influence of numerical information on user behaviors? To solve above issues, we propose a novel multi-modal session-based recommendation (MMSBR) that models both descriptive and numerical information under a unified framework. Specifically, a pseudo-modality contrastive learning is devised to enhance the representation learning of descriptive information. Afterwards, a hierarchical pivot transformer is presented to fuse heterogeneous descriptive information. Moreover, we represent numerical information with Gaussian distribution and design a Wasserstein self-attention to handle the probabilistic influence mode. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed MMSBR. Further analysis also proves that our MMSBR can alleviate the cold-start problem in SBR effectively.

Session-based recommendation intends to predict next purchased items based on anonymous behavior sequences. Numerous economic studies have revealed that item price is a key factor influencing user purchase decisions. Unfortunately, existing methods for session-based recommendation only aim at capturing user interest preference, while ignoring user price preference. Actually, there are primarily two challenges preventing us from accessing price preference. First, the price preference is highly associated to various item features (i.e., category and brand), which asks us to mine price preference from heterogeneous information. Second, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling. To handle above challenges, we propose a novel approach Bi-Preference Learning Heterogeneous Hypergraph Networks (BiPNet) for session-based recommendation. Specifically, the customized heterogeneous hypergraph networks with a triple-level convolution are devised to capture user price and interest preference from heterogeneous features of items. Besides, we develop a Bi-Preference Learning schema to explore mutual relations between price and interest preference and collectively learn these two preferences under the multi-task learning architecture. Extensive experiments on multiple public datasets confirm the superiority of BiPNet over competitive baselines. Additional research also supports the notion that the price is crucial for the task.

Humor recognition is one of the hot topics in the field of natural language processing in recent years. Most existing methods focus on whether something is humorous, while there is less research on humor level recognition. This article proposes a method based on prompt learning and contrastive learning for humor level recognition. Firstly, the context with prompt learning templates and the context without prompt learning templates are input into a pre-trained language model separately. Then, during the fine-tuning stage, a prompt learning strategy based on P-tuning is used to learn semantic information related to humor levels in the text. Subsequently, a contrastive learning loss function is introduced to increase the distance between the vector representations of different classes and reduce the classification difficulty of strong humor and weak humor samples. Finally, a multi-task learning strategy is used to simultaneously perform prompt learning, contrastive learning, and sentence binary classification tasks. Experiments on the Reddit public humor dataset show that the model’s accuracy on the corpus improves by 1.1% compared to the previous best results. Experimental results indicate that the model proposed in this article can effectively recognize humor levels.

Pre-trained language models have become the prevailing approach for handling natural language processing tasks in recent years. Given the similarities in sequential features between symbolic music and natural language text, it is fairly logical to adopt pre-training methods to symbolic music data. However, the disparity between music and natural language text makes it difficult to comprehensively model the unique features of music through traditional text-based pre-training strategies alone. To address this challenge, in this paper, we design the quad-attribute masking (QM) strategy and propose the key prediction (KP) task to improve the extraction of generic knowledge from symbolic music. We evaluate the impact of various pre-training strategies on several public symbolic music datasets, and the results of our experiments reveal that the proposed multi-task pre-training model can effectively capture music domain knowledge from symbolic music data and significantly improve performance on downstream tasks.

Memes have become the popular means of communication for Internet users worldwide. Understanding the Internet meme is one of the most tricky challenges in natural language processing (NLP) tasks due to its convenient non-standard writing and network vocabulary. Recently, many linguists suggested that memes contain rich metaphorical information. However, the existing researches ignore this key feature. Therefore, to incorporate informative metaphors into the meme analysis, we introduce a novel multimodal meme dataset called MET-Meme, which is rich in metaphorical features. It contains 10045 text-image pairs, with manual annotations of the metaphor occurrence, sentiment categories, intentions, and offensiveness degree. Moreover, we propose a range of strong baselines to demonstrate the importance of combining metaphorical features for meme sentiment analysis and semantic understanding tasks, respectively. MET-Meme, and its code are released publicly for research in \urlhttps://github.com/liaolianfoka/MET-Meme-A-Multi-modal-Meme-Dataset-Rich-in-Metaphors.

Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices.

Semantic network is a network of concepts connected by semantic relations. It contains two forms ofbinary semantic networkandmultiplex semantic network. The associated fact prediction is a link prediction task that aims to infer the implicitly connected facts by mining the high-level representation of the network. Previous methods for associated fact prediction put much emphasis on the topological feature of network but not utilize the information of semantic expression. This paper proposes aSemanticNetworkEncoder (SemNE), which learns a feature mapping function from the binary semantic networks and can be applied to the multiplex semantic networks in a pre-training manner. SemNE is a two-stage framework that contains an embedding encoder and a prediction decoder. It jointly models the semantic information and network topology to enrich the network representation. A word self-organization method based on the factual boundary is proposed to unify the topological feature and the semantic feature representations. Experimental results on binary semantic networks show that SemNE achieves the state-of-the-art results in associated fact prediction and experimental results on multiplex semantic networks show that SemNE is scalable and can effectively improve the performance of existing models.

Recommendation systems can help users process large amounts of information, and generative adversarial networks (GANs) show great potential in recommendation systems. In this paper, we propose a new GAN model to enhance the information flow within the generator based on the information flow between the original generator and discriminator. Our experimental results indicate that our model reduces the discrepancy between the generator and the discriminator. Both the generator and discriminator yield considerable performance improvements compared to other strong baselines. The improvements by [email protected] and MRR are significant, which can reach 30.98% and 30.17%, respectively.

Heterographic pun plays a critical role in human writing and literature, which usually has a similar sounding or spelling structure. It is important and difficult research to recognize the heterographic pun because of the ambiguity. However, most existing methods for this task only focus on designing features with rule-based or machine learning methods. In this paper, we propose an end-to-end computational approach - Pronunciation Spelling Understanding Gated Attention (PSUGA) network. For pronunciation, we exploit the hierarchical attention model with phoneme embedding. While for spelling, we consider the character-level, word-level, tag-level, position-level and contextual-level embedding with attention model. To deal with the two parts, we present a gated attention mechanism to control the information integration. We have conducted extensive experiments on SemEval2017 task7 and Pun of the Day datasets. Experimental results show that our approach significantly outperforms state-of-the-art methods.

Facial appearance matters in social networks. Individuals frequently make trait judgments from facial clues. Although these face-based impressions lack the evidence to determine validity, they are of vital importance, because they may relate to human network-based social behavior, such as seeking certain individuals for help, advice, dating, and cooperation, and thus they may relate to centrality in social networks. However, little to no work has investigated the apparent facial traits that influence network centrality, despite the large amount of research on attributions of the central position including personality and behavior. In this paper, we examine whether perceived traits based on facial appearance affect network centrality by exploring the initial stage of social network formation in a first-year college residential area. We took face photos of participants who are freshmen living in the same residential area, and we asked them to nominate community members linking to different networks. We then collected facial perception data by requiring other participants to rate facial images for three main attributions: dominance, trustworthiness, and attractiveness. Meanwhile, we proposed a framework to discover how facial appearance affects social networks. Our results revealed that perceived facial traits were correlated with the network centrality and that they were indicative to predict the centrality of people in different networks. Our findings provide psychological evidence regarding the interaction between faces and network centrality. Our findings also offer insights in to a combination of psychological and social network techniques, and they highlight the function of facial bias in cuing and signaling social traits. To the best of our knowledge, we are the first to explore the influence of facial perception on centrality in social networks.

Learning to rank utilizes machine learning methods to solve ranking problems by constructing ranking models in a supervised way, which needs fixed-length feature vectors of documents as inputs, and outputs the ranking models learned by iteratively reducing the pre-defined ranking loss. The document features are always extracted based on classic textual statistics, and different features contribute differently to ranking performance. Given that well-defined features would contribute more to the retrieval performance, we investigate the usage of autoencoders to enrich the feature representations of documents. Autoencoders, as basic building blocks of deep neural networks, have been successfully used in many text mining tasks for generating effective features. To enrich the feature space for learning to rank, we introduce supervision into the loss functions of autoencoders. Specifically, we first train a linear ranking model on the training data, and then incorporate the learned weights into the reconstruction costs of an autoencoder. Meanwhile, we accumulate the costs of documents for a given query with query-level constraints for producing more useful features. We evaluate the effectiveness of our model on three LETOR datasets, and show that our model can generate effective document features to improve the retrieval performance.

Pseudo relevance feedback, as an effective query expansion method, can significantly improve information retrieval performance. However, the method may negatively impact the retrieval performance when some irrelevant terms are used in the expanded query. Therefore, it is necessary to refine the expansion terms. Learning to rank methods have proven effective in information retrieval to solve ranking problems by ranking the most relevant documents at the top of the returned list, but few attempts have been made to employ learning to rank methods for term refinement in pseudo relevance feedback. This article proposes a novel framework to explore the feasibility of using learning to rank to optimize pseudo relevance feedback by means of reranking the candidate expansion terms. We investigate some learning approaches to choose the candidate terms and introduce some state‐of‐the‐art learning to rank methods to refine the expansion terms. In addition, we propose two term labeling strategies and examine the usefulness of various term features to optimize the framework. Experimental results with three TREC collections show that our framework can effectively improve retrieval performance.

Ranking plays an important role in information retrieval system. In recent years, a kind of research named 'learning to rank' becomes more and more popular, which applies machine learning technology to solve ranking problems. Lots of ranking models belonged to learning to rank have been proposed, such as Regression, RankNet, and ListNet. Inspired by this, we proposed a novel learning to rank algorithm named GPQ in this paper, in which genetic programming was employed to directly optimize Q-measure evaluation metric. Experimental results on OHSUMED benchmark dataset indicated that our method GPQ could be competitive with Ranking SVM, SVMMAP and ListNet, and improve the ranking accuracies.

The central issue in language model estimation is smoothing, which is a technique for avoiding zero probability estimation problem and overcoming data sparsity. There are three representative smoothing methods: Jelinek‐Mercer (JM) method; Bayesian smoothing using Dirichlet priors (Dir) method; and absolute discounting (Dis) method, whose parameters are usually estimated empirically. Previous research in information retrieval (IR) on smoothing parameter estimation tends to select a single value from optional values for the collection, but it may not be appropriate for all the queries. The effectiveness of all the optional values should be considered to improve the ranking performance. Recently, learning to rank has become an effective approach to optimize the ranking accuracy by merging the existing retrieval methods. In this article, the smoothing methods for language modeling in information retrieval (LMIR) with different parameters are treated as different retrieval methods, then a learning to rank approach to learn a ranking model based on the features extracted by smoothing methods is presented. In the process of learning, the effectiveness of all the optional smoothing parameters is taken into account for all queries. The experimental results on the Learning to Rank for Information Retrieval (LETOR) LETOR3.0 and LETOR4.0 data sets show that our approach is effective in improving the performance of LMIR.

With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high‐quality and meaningful topical communities.

Wikipedia is characterized by its dense link structure and a large number of articles in different languages, which make it a notableWeb corpus for knowledge extraction and mining, in particular for mining the multilingual associations. In this paper, motivated by a psychological theory of word meaning, we propose a graph‐based approach to constructing a cross‐language association dictionary (CLAD) fromWikipedia, which can be used in a variety of cross‐language accessing and processing applications. In order to evaluate the quality of the minedCLAD, and to demonstrate how the minedCLADcan be used in practice, we explore two different applications of the minedCLADto cross‐language information retrieval (CLIR). First, we use the minedCLADto conduct cross‐language query expansion; and, second, we use it to filter out translation candidates with low translation probabilities. Experimental results on a variety of standardCLIRtest collections show that theCLIRretrieval performance can be substantially improved with the above two applications ofCLAD, which indicates that the minedCLADis of sound quality.

Learning to rank algorithms are usually grouped into three types: the point wise approach, the pairwise approach, and the listwise approach, according to the input spaces. Much of the prior work is based on the three approaches to learn the ranking model to predict the relevance of a document to a query. In this paper, we focus on the problem of constructing new input space based on groups of documents with the same relevance judgment. A novel approach is proposed based on cross entropy to improve the existing ranking method. The experimental results show that our approach leads to significant improvements in retrieval effectiveness.

It is commonly believed that query logs from Web search are a gold mine for search business, because they reflect users' preference over Web pages presented by search engines, so a lot of studies based on query logs have been carried out in the last few years. In this study, we assume that two queries are relevant to each other when they have same clicked page in their result lists, and we also consider the queries' topics of user's need. Thus, we propose a Two-Stage SimRank (called TSS in this paper) algorithm based on SimRank and some clustering algorithms to compute the similarity among queries, and then use it to discover relevant terms for query expansion, considering the information of topics and the global relationships of queries concurrently, with a query log collected by a practical search engine. Experimental results on two TREC test collections show that our approach can discover qualified terms effectively and improve retrieval performance.

Automatic query expansion technologies have been proven to be effective in many information retrieval tasks. Most existing approaches are based on the assumption that the most informative terms in top-retrieved documents can be viewed as context of the query and thus can be used for query expansion. One problem with these approaches is that some of the expansion terms extracted from feedback documents are irrelevant to the query, and thus may hurt the retrieval performance. In social annotations, users provide different keywords describing the respective Web pages from various aspects. These features may be used to boost IR performance. However, to date, the potential of social annotation for this task has been largely unexplored. In this paper, we explore the possibility and potential of social annotation as a new resource for extracting useful expansion terms. In particular, we propose a term ranking approach based on social annotation resource. The proposed approach consists of two phases: (1) in the first phase, we propose a term-dependency method to choose the most likely expansion terms; (2) in the second phase, we develop a machine learning method for term ranking, which is learnt from the statistics of the candidate expansion terms, using ListNet. Experimental results on three TREC test collections show that the retrieval performance can be improved when the term ranking method is used. In addition, we also demonstrate that terms selected by the term-dependency method from social annotation resources are beneficial to improve the retrieval performance.

In this paper, we use query-level regression as the loss function. The regression loss function has been used in pointwise methods, however pointwise methods ignore the query boundaries and treat the data equally across queries, and thus the effectiveness is limited. We show that regression is an effective loss function for learning to rank when used in query-level. We use neural network to model the ranking function and gradient descent for optimization and refer our method as ListReg. Experimental results show that ListReg significantly outperforms pointwise Regression and the state-of-the-art listwise method in most cases.

Abstract Pseudo‐relevance feedback (PRF) via query expansion (QE) assumes that the top‐ranked documents from the first‐pass retrieval are relevant. The most informative terms in the pseudo‐relevant feedback documents are then used to update the original query representation in order to boost the retrieval performance. Most current PRF approaches estimate the importance of the candidate expansion terms based on their statistics on document level. However, a document for PRF may consist of different topics, which may not be all related to the query even if the document is judged relevant. The main argument of this article is the proposal to conduct PRF on a granularity smaller than on the document level. In this article, we propose a topic‐based feedback model with three different strategies for finding a good query‐related topic based on the Latent Dirichlet Allocation model. The experimental results on four representative TREC collections show that QE based on the derived topic achieves statistically significant improvements over a strong feedback model in the language modeling framework, which updates the query representation based on the top‐ranked documents.

An essential issue in document retrieval is ranking, and the documents are ranked by their expected relevance to a given query. Multiple labels are used to represent different level of relevance for documents to a given query, and the corresponding label values are used to quantify the relevance of the documents. According to the training set for a given query, the documents can be divided into several groups. Specifically, the documents with the same label are assigned to the same group. If the documents in the group with higher relevance label can always be ranked higher over the ones in groups with lower relevance label by a ranking model, it is reasonable to expect perfect ranking performance. Inspired by this idea, we propose a novel framework for learning to rank, which depends on two new samples. The first one is one-group constituted by one document with higher level label and a group of documents with lower level label; the second one is group-group constituted by a group of documents with higher level label and a group of documents with lower level label. A novel loss function is proposed based on the likelihood loss similar to ListMLE. We demonstrate the advantages of our approaches on the Letor 3.0 data set. Experimental results show that our approaches are effective in improving the ranking performance.

In this paper, we propose a graph-based approach to constructing a multilingual association dictionary from Wikipedia, in which we exploit two kinds of links in Wikipedia articles to associate multilingual words and concepts together in a graph. The mined association dictionary is applied in cross language information retrieval (CLIR) to verify its quality. We evaluate our approach on four CLIR data sets and the experimental results show that it is possible to mine a good multilingual association dictionary from Wikipedia articles.