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Average User-side Counterfactual Fairness for Collaborative Filtering ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-04-11 Pengyang Shao, Le Wu, Kun Zhang, Defu Lian, Richang Hong, Yong Li, Meng Wang
Recently, the user-side fairness issue in Collaborative Filtering (CF) algorithms has gained considerable attention, arguing that results should not discriminate an individual or a sub user group based on users’ sensitive attributes (e.g., gender). Researchers have proposed fairness-aware CF models by decreasing statistical associations between predictions and sensitive attributes. A more natural idea
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Document-Level Relation Extraction with Progressive Self-Distillation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-04-08 Quan Wang, Zhendong Mao, Jie Gao, Yongdong Zhang
Document-level relation extraction (RE) aims to simultaneously predict relations (including no-relation cases denoted as NA) between all entity pairs in a document. It is typically formulated as a relation classification task with entities pre-detected in advance and solved by a hard-label training regime, which however neglects the divergence of the NA class and the correlations among other classes
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FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-04-05 Fei Liu, Chenyang Bu, Haotian Zhang, Le Wu, Kui Yu, Xuegang Hu
In educational data mining, knowledge tracing (KT) aims to model learning performance based on student knowledge mastery. Deep-learning-based KT models perform remarkably better than traditional KT and have attracted considerable attention. However, most of them lack interpretability, making it challenging to explain why the model performed well in the prediction. In this paper, we propose an interpretable
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Cross-domain NER under a Divide-and-Transfer Paradigm ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-04-02 Xinghua Zhang, Bowen Yu, Xin Cong, Taoyu Su, Quangang Li, Tingwen Liu, Hongbo Xu
Cross-domain Named Entity Recognition (NER) transfers knowledge learned from a rich-resource source domain to improve the learning in a low-resource target domain. Most existing works are designed based on the sequence labeling framework, defining entity detection and type prediction as a monolithic process. However, they typically ignore the discrepant transferability of these two sub-tasks: the former
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Personality-affected Emotion Generation in Dialog Systems ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-04-03 Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Ruosong Yang, Shuaiqi Liu, Maosong Sun
Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, which will decrease user engagement and service quality
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Toward Bias-Agnostic Recommender Systems: A Universal Generative Framework ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-04-02 Zhidan Wang, Lixin Zou, Chenliang Li, Shuaiqiang Wang, Xu Chen, Dawei Yin, Weidong Liu
User behavior data, such as ratings and clicks, has been widely used to build personalizing models for recommender systems. However, many unflattering factors (e.g., popularity, ranking position, users’ selection) significantly affect the performance of the learned recommendation model. Most existing work on unbiased recommendation addressed these biases from sample granularity (e.g., sample reweighting
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SSR: Solving Named Entity Recognition Problems via a Single-stream Reasoner ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-04-01 Yuxiang Zhang, Junjie Wang, Xinyu Zhu, Tetsuya Sakai, Hayato Yamana
Information Extraction (IE) focuses on transforming unstructured data into structured knowledge, of which Named Entity Recognition (NER) is a fundamental component. In the realm of Information Retrieval (IR), effectively recognizing entities can substantially enhance the precision of search and recommendation systems. Existing methods frame NER as a sequence labeling task, which requires extra data
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Beyond Relevance: Factor-level Causal Explanation for User Travel Decisions with Counterfactual Data Augmentation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Hanzhe Li, Jingjing Gu, Xinjiang Lu, Dazhong Shen, Yuting Liu, YaNan Deng, Guoliang Shi, Hui Xiong
Point-of-Interest (POI) recommendation, an important research hotspot in the field of urban computing, plays a crucial role in urban construction. While understanding the process of users’ travel decisions and exploring the causality of POI choosing is not easy due to the complex and diverse influencing factors in urban travel scenarios. Moreover, the spurious explanations caused by severe data sparsity
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Listwise Generative Retrieval Models via a Sequential Learning Process ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng
Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identifiers (docids) given a query. Existing generative retrieval (GR) models commonly employ maximum likelihood estimation (MLE) for optimization: this involves maximizing the likelihood of a single relevant docid given an input
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Deep Coupling Network For Multivariate Time Series Forecasting ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-21 Kun Yi, Qi Zhang, Hui He, Kaize Shi, Liang Hu, Ning An, Zhendong Niu
Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order interactions present within and between time
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Privacy-Preserving Cross-Domain Recommendation with Federated Graph Learning ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-21 Changxin Tian, Yuexiang Xie, Xu Chen, Yaliang Li, Wayne Xin Zhao
As people inevitably interact with items across multiple domains or various platforms, cross-domain recommendation (CDR) has gained increasing attention. However, the rising privacy concerns limit the practical applications of existing CDR models since they assume that full or partial data are accessible among different domains. Recent studies on privacy-aware CDR models neglect the heterogeneity from
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Passage-aware Search Result Diversification ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-21 Zhan Su, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen
Research on search result diversification strives to enhance the variety of subtopics within the list of search results. Existing studies usually treat a document as a whole and represent it with one fixed-length vector. However, considering that a long document could cover different aspects of a query, using a single vector to represent the document is usually insufficient. To tackle this problem
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MCN4Rec: Multi-level Collaborative Neural Network for Next Location Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Shuzhe Li, Wei Chen, Bin Wang, Chao Huang, Yanwei Yu, Junyu Dong
Next location recommendation plays an important role in various location-based services, yielding great value for both users and service providers. Existing methods usually model temporal dependencies with explicit time intervals or learn representation from customized point of interest (POI) graphs with rich context information to capture the sequential patterns among POIs. However, this problem is
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On the Effectiveness of Sampled Softmax Loss for Item Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Jiancan Wu, Xiang Wang, Xingyu Gao, Jiawei Chen, Hongcheng Fu, Tianyu Qiu
The learning objective plays a fundamental role to build a recommender system. Most methods routinely adopt either pointwise (e.g., binary cross-entropy) or pairwise (e.g., BPR) loss to train the model parameters, while rarely pay attention to softmax loss, which assumes the probabilities of all classes sum up to 1, due to its computational complexity when scaling up to large datasets or intractability
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Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 John P. Lalor, Ahmed Abbasi, Kezia Oketch, Yi Yang, Nicole Forsgren
Fairness measurement is crucial for assessing algorithmic bias in various types of machine learning (ML) models, including ones used for search relevance, recommendation, personalization, talent analytics, and natural language processing. However, the fairness measurement paradigm is currently dominated by fairness metrics that examine disparities in allocation and/or prediction error as univariate
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MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat-Seng Chua
Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the modeling of multiple relations among users, bundles, and items. CrossCBR, in particular, incorporates cross-view contrastive learning into a two-view preference
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Can Perturbations Help Reduce Investment Risks? Risk-aware Stock Recommendation via Split Variational Adversarial Training ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Jiezhu Cheng, Kaizhu Huang, Zibin Zheng
In the stock market, a successful investment requires a good balance between profits and risks. Based on the learning to rank paradigm, stock recommendation has been widely studied in quantitative finance to recommend stocks with higher return ratios for investors. Despite the efforts to make profits, many existing recommendation approaches still have some limitations in risk control, which may lead
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Tagging Items with Emerging Tags: A Neural Topic Model Based Few-Shot Learning Approach ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Shangkun Che, Hongyan Liu, Shen Liu
The tagging system has become a primary tool to organize information resources on the Internet, which benefits both users and the platforms. To build a successful tagging system, automatic tagging methods are desired. With the development of society, new tags keep emerging. The problem of tagging items with emerging tags is an open challenge for an automatic tagging system, and it has not been well
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Transferring Causal Mechanism over Meta-representations for Target-Unknown Cross-domain Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Shengyu Zhang, Qiaowei Miao, Ping Nie, Mengze Li, Zhengyu Chen, Fuli Feng, Kun Kuang, Fei Wu
Tackling the pervasive issue of data sparsity in recommender systems, we present an insightful investigation into the burgeoning area of non-overlapping cross-domain recommendation, a technique that facilitates the transfer of interaction knowledge across domains without necessitating inter-domain user/item correspondence. Existing approaches have predominantly depended on auxiliary information, such
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Token-Event-Role Structure-Based Multi-Channel Document-Level Event Extraction ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Qizhi Wan, Changxuan Wan, Keli Xiao, Hui Xiong, Dexi Liu, Xiping Liu, Rong Hu
Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different
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Counterfactual Explanation for Fairness in Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-22 Xiangmeng Wang, Qian Li, Dianer Yu, Qing Li, Guandong Xu
Fairness-aware recommendation alleviates discrimination issues to build trustworthy recommendation systems. Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users’ trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-scale search space and the greedy nature of the explanation
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SPContrastNet: A Self-Paced Contrastive Learning Model for Few-Shot Text Classification ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-20 Junfan Chen, Richong Zhang, Xiaohan Jiang, Chunming Hu
Meta-learning has recently promoted few-shot text classification, which identifies target classes based on information transferred from source classes through a series of small tasks or episodes. Existing works constructing their meta-learner on Prototypical Networks need improvement in learning discriminative text representations between similar classes that may lead to conflicts in label prediction
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Discrete Federated Multi-behavior Recommendation for Privacy-Preserving Heterogeneous One-Class Collaborative Filtering ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-18 Enyue Yang, Weike Pan, Qiang Yang, Zhong Ming
Recently, federated recommendation has become a research hotspot mainly because of users’ awareness of privacy in data. As a recent and important recommendation problem, in heterogeneous one-class collaborative filtering (HOCCF), each user may involve of two different types of implicit feedback, i.e., examinations and purchases. So far, privacy-preserving HOCCF has received relatively little attention
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Distributional Fairness-aware Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-18 Hao Yang, Xian Wu, Zhaopeng Qiu, Yefeng Zheng, Xu Chen
Fairness has been gradually recognized as a significant problem in the recommendation domain. Previous models usually achieve fairness by reducing the average performance gap between different user groups. However, the average performance may not sufficiently represent all the characteristics of the performances in a user group. Thus, equivalent average performance may not mean the recommender model
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DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-18 Xing Tang, Ling Chen, Hongyu Shi, Dandan Lyu
Event prediction is a vital and challenging task in temporal knowledge graphs (TKGs), which have played crucial roles in various applications. Recently, many graph neural networks based approaches are proposed to model the graph structure information in TKGs. However, these approaches only construct graphs based on quadruplets and model the pairwise correlation between entities, which fail to capture
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Diversifying Sequential Recommendation with Retrospective and Prospective Transformers ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-17 Chaoyu Shi, Pengjie Ren, Dongjie Fu, Xin Xin, Shansong Yang, Fei Cai, Zhaochun Ren, Zhumin Chen
Previous studies on sequential recommendation (SR) have predominantly concentrated on optimizing recommendation accuracy. However, there remains a significant gap in enhancing recommendation diversity, particularly for short interaction sequences. The limited availability of interaction information in short sequences hampers the recommender’s ability to comprehensively model users’ intents, consequently
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Collaborative Sequential Recommendations via Multi-View GNN-Transformers ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-15 Tianze Luo, Yong Liu, Sinno Jialin Pan
Sequential recommendation systems aim to exploit users’ sequential behavior patterns to capture their interaction intentions and improve recommendation accuracy. Existing sequential recommendation methods mainly focus on modeling the items’ chronological relationships in each individual user behavior sequence, which may not be effective in making accurate and robust recommendations. On one hand, the
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Cooking with Conversation: Enhancing User Engagement and Learning with a Knowledge-Enhancing Assistant ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-15 Alexander Frummet, Alessandro Speggiorin, David Elsweiler, Anton Leuski, Jeff Dalton
We present two empirical studies to investigate users’ expectations and behaviours when using digital assistants, such as Alexa and Google Home, in a kitchen context: First, a survey (N=200) queries participants on their expectations for the kinds of information that such systems should be able to provide. While consensus exists on expecting information about cooking steps and processes, younger participants
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Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-15 Yunchang Zhu, Liang Pang, Kangxi Wu, Yanyan Lan, Huawei Shen, Xueqi Cheng
Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. While this generally improves performance on average, the extra neurons do not yield a consistent improvement for all instances. This is because some hidden neurons are redundant, and the noise mixed in input neurons tends to
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Multi-grained Document Modeling for Search Result Diversification ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-15 Zhirui Deng, Zhicheng Dou, Zhan Su, Ji-Rong Wen
Search result diversification plays a crucial role in improving users’ search experience by providing users with documents covering more subtopics. Previous studies have made great progress in leveraging inter-document interactions to measure the similarity among documents. However, different parts of the document may embody different subtopics and existing models ignore the subtle similarities and
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ELAKT: Enhancing Locality for Attentive Knowledge Tracing ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-14 Yanjun Pu, Fang Liu, Rongye Shi, Haitao Yuan, Ruibo Chen, Tianhao Peng, WenJun Wu
Knowledge tracing models based on deep learning can achieve impressive predictive performance by leveraging attention mechanisms. However, there still exist two challenges in attentive knowledge tracing: First, the mechanism of classical models of attentive knowledge tracing demonstrates relatively low attention when processing exercise sequences with shifting knowledge concepts, making it difficult
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Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-13 Jian Wang, Dongding Lin, Wenjie Li
Target-oriented proactive dialogue systems aim to lead conversations from a dialogue context toward a pre-determined target, such as making recommendations on designated items or introducing new specific topics. To this end, it is critical for such dialogue systems to plan reasonable actions to drive the conversation proactively, and meanwhile, to plan appropriate topics to move the conversation forward
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Towards Unified Representation Learning for Career Mobility Analysis with Trajectory Hypergraph ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-06 Rui Zha, Ying Sun, Chuan Qin, Le Zhang, Tong Xu, Hengshu Zhu, Enhong Chen
Career mobility analysis aims at understanding the occupational movement patterns of talents across distinct labor market entities, which enables a wide range of talent-centered applications, such as job recommendation, labor demand forecasting, and company competitive analysis. Existing studies in this field mainly focus on a single fixed scale, either investigating individual trajectories at the
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Invisible Black-Box Backdoor Attack against Deep Cross-Modal Hashing Retrieval ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-03-02 Tianshi Wang, Fengling Li, Lei Zhu, Jingjing Li, Zheng Zhang, Heng Tao Shen
Deep cross-modal hashing has promoted the field of multi-modal retrieval due to its excellent efficiency and storage, but its vulnerability to backdoor attacks is rarely studied. Notably, current deep cross-modal hashing methods inevitably require large-scale training data, resulting in poisoned samples with imperceptible triggers that can easily be camouflaged into the training data to bury backdoors
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Few-shot Learning for Heterogeneous Information Networks ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-27 Yang Fang, Xiang Zhao, Weidong Xiao, Maarten de Rijke
Heterogeneous information networks (HINs) are a key resource in many domain-specific retrieval and recommendation scenarios, and in conversational environments. Current approaches to mining graph data often rely on abundant supervised information. However, supervised signals for graph learning tend to be scarce for a new task and only a handful of labeled nodes may be available. Meta-learning mechanisms
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Filter-based Stance Network for Rumor Verification ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-26 Jun Li, Yi Bin, Yunshan Ma, Yang Yang, Zi Huang, Tat-Seng Chua
Rumor verification on social media aims to identify the truth value of a rumor, which is important to decrease the detrimental public effects. A rumor might arouse heated discussions and replies, conveying different stances of users that could be helpful in identifying the rumor. Thus, several works have been proposed to verify a rumor by modelling its entire stance sequence in the time domain. However
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Generalized Weak Supervision for Neural Information Retrieval ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-21 Yen-Chieh Lien, Hamed Zamani, W. Bruce Croft
Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can train NRMs via weak supervision, where a large dataset is automatically generated using an existing ranking model (called the weak labeler) for training
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Improving Semi-Supervised Text Classification with Dual Meta-Learning ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-20 Shujie Li, Guanghu Yuan, Min Yang, Ying Shen, Chengming Li, Ruifeng Xu, Xiaoyan Zhao
The goal of semi-supervised text classification (SSTC) is to train a model by exploring both a small number of labeled data and a large number of unlabeled data, such that the learned semi-supervised classifier performs better than the supervised classifier trained on solely the labeled samples. Pseudo-labeling is one of the most widely used SSTC techniques, which trains a teacher classifier with a
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Causal Inference in Recommender Systems: A Survey and Future Directions ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, Yong Li
Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality, not just correlation, and correlation
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Dense Text Retrieval Based on Pretrained Language Models: A Survey ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Wayne Xin Zhao, Jing Liu, Ruiyang Ren, Ji-Rong Wen
Text retrieval is a long-standing research topic on information seeking, where a system is required to return relevant information resources to user’s queries in natural language. From heuristic-based retrieval methods to learning-based ranking functions, the underlying retrieval models have been continually evolved with the ever-lasting technical innovation. To design effective retrieval models, a
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Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Zhengbang Zhu, Rongjun Qin, Junjie Huang, Xinyi Dai, Yang Yu, Yong Yu, Weinan Zhang
Recommender systems are expected to be assistants that help human users find relevant information automatically without explicit queries. As recommender systems evolve, increasingly sophisticated learning techniques are applied and have achieved better performance in terms of user engagement metrics such as clicks and browsing time. The increase in the measured performance, however, can have two possible
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Triple Sequence Learning for Cross-domain Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Haokai Ma, Ruobing Xie, Lei Meng, Xin Chen, Xu Zhang, Leyu Lin, Jie Zhou
Cross-domain recommendation (CDR) aims at leveraging the correlation of users’ behaviors in both the source and target domains to improve the user preference modeling in the target domain. Conventional CDR methods typically explore the dual-relations between the source and target domains’ behaviors. However, this may ignore the informative mixed behaviors that naturally reflect the user’s global preference
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Using Neural and Graph Neural Recommender Systems to Overcome Choice Overload: Evidence From a Music Education Platform ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Hédi Razgallah, Michalis Vlachos, Ahmad Ajalloeian, Ninghao Liu, Johannes Schneider, Alexis Steinmann
The application of recommendation technologies has been crucial in the promotion of physical and digital content across numerous global platforms such as Amazon, Apple, and Netflix. Our study aims to investigate the advantages of employing recommendation technologies on educational platforms, with a particular focus on an educational platform for learning and practicing music. Our research is based
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Relevance Feedback with Brain Signals ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Ziyi Ye, Xiaohui Xie, Qingyao Ai, Yiqun Liu, Zhihong Wang, Weihang Su, Min Zhang
The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes. However, such signals are indirect indicators of relevance and
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Intent-Oriented Dynamic Interest Modeling for Personalized Web Search ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Yutong Bai, Yujia Zhou, Zhicheng Dou, Ji-Rong Wen
Given a user, a personalized search model relies on her historical behaviors, such as issued queries and their clicked documents, to generate an interest profile and personalize search results accordingly. In interest profiling, most existing personalized search approaches use “static” document representations as the inputs, which do not change with the current search. However, a document is usually
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MCRPL: A Pretrain, Prompt, and Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Hao Liu, Lei Guo, Lei Zhu, Yongqiang Jiang, Min Gao, Hongzhi Yin
Cross-domain Recommendation is the task that tends to improve the recommendations in the sparse target domain by leveraging the information from other rich domains. Existing methods of cross-domain recommendation mainly focus on overlapping scenarios by assuming users are totally or partially overlapped, which are taken as bridges to connect different domains. However, this assumption does not always
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Revisiting Bag of Words Document Representations for Efficient Ranking with Transformers ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 David Rau, Mostafa Dehghani, Jaap Kamps
Modern transformer-based information retrieval models achieve state-of-the-art performance across various benchmarks. The self-attention of the transformer models is a powerful mechanism to contextualize terms over the whole input but quickly becomes prohibitively expensive for long input as required in document retrieval. Instead of focusing on the model itself to improve efficiency, this paper explores
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Predicting Representations of Information Needs from Digital Activity Context ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Tung Vuong, Tuukka Ruotsalo
Information retrieval systems often consider search-session and immediately preceding web-browsing history as the context for predicting users’ present information needs. However, such context is only available when a user’s information needs originate from web context or when users have issued preceding queries in the search session. Here, we study the effect of more extensive context information
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FairGap: Fairness-Aware Recommendation via Generating Counterfactual Graph ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-02-09 Wei Chen, Yiqing Wu, Zhao Zhang, Fuzhen Zhuang, Zhongshi He, Ruobing Xie, Feng Xia
The emergence of Graph Neural Networks (GNNs) has greatly advanced the development of recommendation systems. Recently, many researchers have leveraged GNN-based models to learn fair representations for users and items. However, current GNN-based models suffer from biased user–item interaction data, which negatively impacts recommendation fairness. Although there have been several studies employing
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An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-01-31 Qi Liu, Gang Guo, Jiaxin Mao, Zhicheng Dou, Ji-Rong Wen, Hao Jiang, Xinyu Zhang, Zhao Cao
With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector, the recently proposed late-interaction multi-vector models (i.e., ColBERT
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Robust Collaborative Filtering to Popularity Distribution Shift ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-01-22 An Zhang, Wenchang Ma, Jingnan Zheng, Xiang Wang, Tat-Seng Chua
In leading collaborative filtering (CF) models, representations of users and items are prone to learn popularity bias in the training data as shortcuts. The popularity shortcut tricks are good for in-distribution (ID) performance but poorly generalized to out-of-distribution (OOD) data, i.e., when popularity distribution of test data shifts w.r.t. the training one. To close the gap, debiasing strategies
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Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-01-22 Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, Tao Jiang
Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage of the epidemic. However, there exists an inescapable risk of privacy leakage in the fine-grained user mobility trajectories required by individual-level infection prediction. In this article, we focus on developing a framework of privacy-preserving individual-level infection prediction
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Less is More: Removing Redundancy of Graph Convolutional Networks for Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2024-01-22 Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine
While Graph Convolutional Networks (GCNs) have shown great potential in recommender systems and collaborative filtering (CF), they suffer from expensive computational complexity and poor scalability. On top of that, recent works mostly combine GCNs with other advanced algorithms which further sacrifice model efficiency and scalability. In this work, we unveil the redundancy of existing GCN-based methods
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Bi-preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2023-12-29 Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Yuan Lin, Hongfei Lin
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
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rHDP: An Aspect Sharing-Enhanced Hierarchical Topic Model for Multi-Domain Corpus ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2023-12-29 Yitao Zhang, Changxuan Wan, Keli Xiao, Qizhi Wan, Dexi Liu, Xiping Liu
Learning topic hierarchies from a multi-domain corpus is crucial in topic modeling as it reveals valuable structural information embedded within documents. Despite the extensive literature on hierarchical topic models, effectively discovering inter-topic correlations and differences among subtopics at the same level in the topic hierarchy, obtained from multiple domains, remains an unresolved challenge
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Decoupled Progressive Distillation for Sequential Prediction with Interaction Dynamics ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2023-12-29 Kaixi Hu, Lin Li, Qing Xie, Jianquan Liu, Xiaohui Tao, Guandong Xu
Sequential prediction has great value for resource allocation due to its capability in analyzing intents for next prediction. A fundamental challenge arises from real-world interaction dynamics where similar sequences involving multiple intents may exhibit different next items. More importantly, the character of volume candidate items in sequential prediction may amplify such dynamics, making deep
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H3GNN: Hybrid Hierarchical HyperGraph Neural Network for Personalized Session-based Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2023-12-30 Zhizhuo Yin, Kai Han, Pengzi Wang, Xi Zhu
Personalized Session-based recommendation (PSBR) is a general and challenging task in the real world, aiming to recommend a session’s next clicked item based on the session’s item transition information and the corresponding user’s historical sessions. A session is defined as a sequence of interacted items during a short period. The PSBR problem has a natural hierarchical architecture in which each
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Manipulating Visually Aware Federated Recommender Systems and Its Countermeasures ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2023-12-30 Wei Yuan, Shilong Yuan, Chaoqun Yang, Nguyen Quoc Viet hung, Hongzhi Yin
Federated recommender systems (FedRecs) have been widely explored recently due to their capability to safeguard user data privacy. These systems enable a central server to collaboratively learn recommendation models by sharing public parameters with clients, providing privacy-preserving solutions. However, this collaborative approach also creates a vulnerability that allows adversaries to manipulate
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Better Understanding Procedural Search Tasks: Perceptions, Behaviors, and Challenges ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2023-12-29 Bogeum Choi, Sarah Casteel, Jaime Arguello, Robert Capra
People often search for information to acquire procedural knowledge–“how to” knowledge about step-by-step procedures, methods, algorithms, techniques, heuristics, and skills. A procedural search task might involve implementing a solution to a problem, evaluating different approaches to a problem, and brainstorming on the types of problems that can be solved with a specific resource. We report on a
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DiffuRec: A Diffusion Model for Sequential Recommendation ACM Trans. Inf. Syst. (IF 5.6) Pub Date : 2023-12-29 Zihao Li, Aixin Sun, Chenliang Li
Mainstream solutions to sequential recommendation represent items with fixed vectors. These vectors have limited capability in capturing items’ latent aspects and users’ diverse preferences. As a new generative paradigm, diffusion models have achieved excellent performance in areas like computer vision and natural language processing. To our understanding, its unique merit in representation generation