IEEE Transactions on Knowledge and Data Engineering最新文献_第6页

Discovery of Temporal Network Motifs 时间网络基序的发现

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-04 DOI: 10.1109/TKDE.2025.3538514

Hanqing Chen;Shuai Ma;Junfeng Liu;Lizhen Cui

{"title":"Discovery of Temporal Network Motifs","authors":"Hanqing Chen;Shuai Ma;Junfeng Liu;Lizhen Cui","doi":"10.1109/TKDE.2025.3538514","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538514","url":null,"abstract":"Network motifs provide a deep insight into the network functional abilities, and have proven useful in various practical applications. Existing studies reveal that different definitions of motifs may be needed for different temporal networks. In this study, we focus on a class of temporal networks such that the nodes and edges keep fixed, but the edge labels vary regularly with timestamps. First, we propose a proper definition of temporal motifs, which appear continuously within sufficiently large time intervals, to properly reinterpret the recurrent and statistically significant nature of motifs in temporal networks. Second, we develop a low polynomial time solution to find temporal motifs for all possible time intervals with the top to bottom and right to left scheme, based on the analyses of the properties for temporal motifs. Third, we develop a theoretically faster incremental solution to efficiently find temporal motifs to support continuously updates of temporal networks, by identifying unaffected time intervals and unnecessary edges. Finally, we have conducted extensive experiments to verify the efficiency and usefulness of our static and incremental solutions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2376-2390"},"PeriodicalIF":8.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Large-Scale Hierarchical Causal Discovery via Weak Prior Knowledge 基于弱先验知识的大规模层次因果发现

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-04 DOI: 10.1109/TKDE.2025.3537832

Xiangyu Wang;Taiyu Ban;Lyuzhou Chen;Derui Lyu;Qinrui Zhu;Huanhuan Chen

{"title":"Large-Scale Hierarchical Causal Discovery via Weak Prior Knowledge","authors":"Xiangyu Wang;Taiyu Ban;Lyuzhou Chen;Derui Lyu;Qinrui Zhu;Huanhuan Chen","doi":"10.1109/TKDE.2025.3537832","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3537832","url":null,"abstract":"Causal discovery faces significant challenges as the number of hypotheses grows exponentially with the number of variables. This complexity becomes particularly daunting when dealing with large sets of variables. We introduce a novel divide-and-conquer method that uniquely handles this challenge. The existing division strategies often rely on conditional independency (CI) tests or data-driven clustering to split variables, which can suffer from the typical data scarcity in large-scale settings, thus leading to inaccurate division results. The proposed method overcomes this by implementing a data-independent division strategy, which constructs a prior structure, informed by potential causal relationships identified using a Large Language Model (LLM), to guide recursively dividing variables into sub-sets. This approach avoids the impact of data insufficiency and is robust against potential incompleteness in the prior structure. In the merging phase, we adopt a score-based refinement strategy to address fake causal links caused by hidden variables in sub-sets, which eliminates edges in the intersected parts of sub-sets to optimize the score of local structures. While maintaining both correctness and completeness under the faithfulness assumption, this novel merging approach demonstrates enhanced performance than the conventional CI-test based merging strategy in practical scenarios. Empirical evaluations on various large-scale datasets demonstrate the proposed approach's superior accuracy and efficiency compared to existing causal discovery methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2695-2711"},"PeriodicalIF":8.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Boosting GNN-Based Link Prediction via PU-AUC Optimization 通过 PU-AUC 优化提升基于 GNN 的链接预测能力

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-03 DOI: 10.1109/TKDE.2025.3525490

Yuren Mao;Yu Hao;Xin Cao;Yunjun Gao;Chang Yao;Xuemin Lin

{"title":"Boosting GNN-Based Link Prediction via PU-AUC Optimization","authors":"Yuren Mao;Yu Hao;Xin Cao;Yunjun Gao;Chang Yao;Xuemin Lin","doi":"10.1109/TKDE.2025.3525490","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3525490","url":null,"abstract":"Link prediction, which aims to predict the existence of a link between two nodes in a network, has various applications ranging from friend recommendation to protein interaction prediction. Recently, Graph Neural Network (GNN)-based link prediction has demonstrated its advantages and achieved the state-of-the-art performance. Typically, GNN-based link prediction can be formulated as a binary classification problem. However, in link prediction, we only have positive data (observed links) and unlabeled data (unobserved links), but no negative data. Therefore, Positive Unlabeled (PU) learning naturally fits the link prediction scenario. Unfortunately, the unknown class prior and data imbalance of networks impede the use of PU learning in link prediction. To deal with these issues, this paper proposes a novel model-agnostic PU learning algorithm for GNN-based link prediction by means of <italic>Positive-Unlabeled Area Under the Receiver Operating Characteristic Curve (PU-AUC) optimization. The proposed method is free of class prior estimation and able to handle the data imbalance. Moreover, we propose an accelerated method to reduce the operational complexity of PU-AUC optimization from quadratic to approximately linear. Extensive experiments back up our theoretical analysis and validate that the proposed method is capable of boosting the performance of the state-of-the-art GNN-based link prediction models.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1635-1649"},"PeriodicalIF":8.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CLEAR: Spatial-Temporal Traffic Data Representation Learning for Traffic Prediction 用于交通预测的时空交通数据表示学习

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-03 DOI: 10.1109/TKDE.2025.3536009

James Jianqiao Yu;Xinwei Fang;Shiyao Zhang;Yuxin Ma

{"title":"CLEAR: Spatial-Temporal Traffic Data Representation Learning for Traffic Prediction","authors":"James Jianqiao Yu;Xinwei Fang;Shiyao Zhang;Yuxin Ma","doi":"10.1109/TKDE.2025.3536009","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3536009","url":null,"abstract":"In the evolving field of urban development, precise traffic prediction is essential for optimizing traffic and mitigating congestion. While traditional graph learning-based models effectively exploit complex spatial-temporal correlations, their reliance on trivially generated graph structures or deeply intertwined adjacency learning without supervised loss significantly impedes their efficiency. This paper presents Contrastive Learning of spatial-tEmporal trAffic data Representations (CLEAR) framework, a comprehensive approach to spatial-temporal traffic data representation learning aimed at enhancing the accuracy of traffic predictions. Employing self-supervised contrastive learning, CLEAR strategically extracts discriminative embeddings from both traffic time-series and graph-structured data. The framework applies weak and strong data augmentations to facilitate subsequent exploitations of intrinsic spatial-temporal correlations that are critical for accurate prediction. Additionally, CLEAR incorporates advanced representation learning models that transmute these dynamics into compact, semantic-rich embeddings, thereby elevating downstream models’ prediction accuracy. By integrating with existing traffic predictors, CLEAR boosts predicting performance and accelerates the training process by effectively decoupling adjacency learning from correlation learning. Comprehensive experiments validate that CLEAR can robustly enhance the capabilities of existing graph learning-based traffic predictors and provide superior traffic predictions with a straightforward representation decoder. This investigation highlights the potential of contrastive representation learning in developing robust traffic data representations for traffic prediction.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1672-1687"},"PeriodicalIF":8.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Group-Aware Dynamic Graph Representation Learning for Next POI Recommendation 下一个POI推荐的群体感知动态图表示学习

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-03 DOI: 10.1109/TKDE.2025.3538005

Ruichang Li;Xiangwu Meng;Yujie Zhang

{"title":"Group-Aware Dynamic Graph Representation Learning for Next POI Recommendation","authors":"Ruichang Li;Xiangwu Meng;Yujie Zhang","doi":"10.1109/TKDE.2025.3538005","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538005","url":null,"abstract":"The Next POI recommendation, which has attracted many attentions recently, is a complex study due to the sparsity of check-in data and numerous sequential patterns. The recent methods based on sequential modeling have shown promising applicability for this task. However, most of existing next POI recommendation researches only model users’ preferences based on their own sequences and ignore the influence of partners who visit POI with the target user and may change with time. Inspired by dynamic Graph neural networks, we propose a Group-aware Dynamic Graph Representation Learning (GDGRL) method for next POI recommendation. GDGRL connects different user sequences and specific partners via dynamic graph structure, which contains interactions between users and POIs as well as influence of partners. The users’ dynamic preferences are learned from group-aware dynamic graph and context-aware dynamic graph through dynamic graph neural networks. Finally, the next POI recommendation task is transformed into a link prediction between user node and POI node in the dynamic graph. Extensive experiments on two real-world datasets show that GDGRL outperforms several state-of-the-art approaches.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2614-2625"},"PeriodicalIF":8.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond 大型语言模型真的是好的逻辑推理器吗？综合评价及超越

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-03 DOI: 10.1109/TKDE.2025.3536008

Fangzhi Xu;Qika Lin;Jiawei Han;Tianzhe Zhao;Jun Liu;Erik Cambria

{"title":"Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond","authors":"Fangzhi Xu;Qika Lin;Jiawei Han;Tianzhe Zhao;Jun Liu;Erik Cambria","doi":"10.1109/TKDE.2025.3536008","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3536008","url":null,"abstract":"Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) have emerged as a noteworthy innovation in natural language processing (NLP). However, the question of whether LLMs can effectively address the task of logical reasoning, which requires gradual cognitive inference similar to human intelligence, remains unanswered. To this end, we aim to bridge this gap and provide comprehensive evaluations in this paper. First, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings. Considering the comprehensiveness of evaluations, we include 3 early-era representative LLMs and 4 trending LLMs. Second, different from previous evaluations relying only on simple metrics (e.g., <italic>accuracy), we propose fine-level evaluations in objective and subjective manners, covering both answers and explanations, including <italic>answer correctness, <italic>explain correctness, <italic>explain completeness and <italic>explain redundancy. Additionally, to uncover the logical flaws of LLMs, problematic cases will be attributed to five error types from two dimensions, i.e., <italic>evidence selection process and <italic>reasoning process. Third, to avoid the influences of knowledge bias and concentrate purely on benchmarking the logical reasoning capability of LLMs, we propose a new dataset with neutral content. Based on the in-depth evaluations, this paper finally forms a general evaluation scheme of logical reasoning capability from six dimensions (i.e., <italic>Correct, <italic>Rigorous, <italic>Self-aware, <italic>Active, <italic>Oriented and <italic>No hallucination). It reflects the pros and cons of LLMs and gives guiding directions for future works.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1620-1634"},"PeriodicalIF":8.9,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pattern Hiding and Authorized Searchable Encryption for Data Sharing in Cloud Storage 云存储中数据共享的模式隐藏和授权可搜索加密

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-01-31 DOI: 10.1109/TKDE.2025.3537613

Kai Zhang;Boli Hu;Jianting Ning;Junqing Gong;Haifeng Qian

{"title":"Pattern Hiding and Authorized Searchable Encryption for Data Sharing in Cloud Storage","authors":"Kai Zhang;Boli Hu;Jianting Ning;Junqing Gong;Haifeng Qian","doi":"10.1109/TKDE.2025.3537613","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3537613","url":null,"abstract":"Secure cloud storage is a prevalent way to provide data retrieval services, where users’ data are encrypted before uploading to the cloud. To effectively perform keyword searches over the encrypted data, the approach of searchable encryption (SE) was introduced. However, the leakage of the keyword-pair result pattern to the cloud could be exploited to reconstruct the queried keywords. To mitigate such information leakages, numerous result pattern-hiding SE systems were proposed but rarely supported data sharing with expressive queries and even owner-enforced authorization. Therefore, we present a result pattern hiding and authorized SE system (AXT) supporting conjunctive queries for cloud-based data sharing. Technically, we construct an authorized label private set intersection protocol from a refined authorized public key encryption with an equality test and then combine it with an introduced asymmetric variant of oblivious cross-tag protocol. Moreover, we introduce the system and security model of AXT along with rigorous security proof. Furthermore, we conduct comparative experiments between state-of-the-art solutions with AXT on HUAWEI Cloud platform under the widely recognized Enron dataset, which reveal that AXT achieves practical performance with retaining authorized data sharing and result pattern hiding, specifically, the time overhead for conjunctive queries with 10 keywords is reduced by 20<inline-formula><tex-math>$%$</tex-math></inline-formula>.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2802-2815"},"PeriodicalIF":8.9,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaboratively Semantic Alignment and Metric Learning for Cross-Modal Hashing 跨模态哈希的协同语义对齐和度量学习

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-01-31 DOI: 10.1109/TKDE.2025.3537704

Jiaxing Li;Wai Keung Wong;Lin Jiang;Kaihang Jiang;Xiaozhao Fang;Shengli Xie;Jie Wen

{"title":"Collaboratively Semantic Alignment and Metric Learning for Cross-Modal Hashing","authors":"Jiaxing Li;Wai Keung Wong;Lin Jiang;Kaihang Jiang;Xiaozhao Fang;Shengli Xie;Jie Wen","doi":"10.1109/TKDE.2025.3537704","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3537704","url":null,"abstract":"Cross-modal retrieval is a promising technique nowadays to find semantically similar instances in other modalities while a query instance is given from one modality. However, there still exists many challenges for reducing heterogeneous modality gap by embedding label information to discrete hash codes effectively, solving the binary optimization when generating unified hash codes and reducing the discrepancy of data distribution efficiently during common space learning. In order to overcome the above-mentioned challenges, we propose a Collaboratively Semantic alignment and Metric learning for cross-modal Hashing (CSMH) in this paper. Specifically, by a kernelization operation, CSMH first extracts the non-linear data features for each modality, which are projected into a latent subspace to align both marginal and conditional distributions simultaneously. Then, a maximum mean discrepancy-based metric strategy is customized to mitigate the distribution discrepancies among features from different modalities. Finally, semantic information obtained from the label similarity matrix, is further incorporated to embed the latent semantic structure into the discriminant subspace. Experimental results of CSMH and baseline methods on four widely-used datasets show that CSMH outperforms some state-of-the-art hashing baseline methods for cross-modal retrieval on efficiency and precision.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2311-2328"},"PeriodicalIF":8.9,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity Cafe：利用缺失数据异构性改进联邦数据插入

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-01-30 DOI: 10.1109/TKDE.2025.3537403

Sitao Min;Hafiz Asif;Xinyue Wang;Jaideep Vaidya

{"title":"Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity","authors":"Sitao Min;Hafiz Asif;Xinyue Wang;Jaideep Vaidya","doi":"10.1109/TKDE.2025.3537403","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3537403","url":null,"abstract":"Federated learning (FL), a decentralized machine learning approach, offers great performance while alleviating autonomy and confidentiality concerns. Despite FL’s popularity, how to deal with missing values in a federated manner is not well understood. In this work, we initiate a study of federated imputation of missing values, particularly in complex scenarios, where missing data heterogeneity exists and the state-of-the-art (SOTA) approaches for federated imputation suffer from significant loss in imputation quality. We propose Cafe, a personalized FL approach for missing data imputation. Cafe is inspired from the observation that heterogeneity can induce differences in observable and missing data distribution across clients, and that these differences can be leveraged to improve the imputation quality. Cafe computes personalized weights that are automatically calibrated for the level of heterogeneity, which can remain unknown, to develop personalized imputation models for each client. An extensive empirical evaluation over a variety of settings demonstrates that Cafe matches the performance of SOTA baselines in homogeneous settings while significantly outperforming the baselines in heterogeneous settings.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2266-2281"},"PeriodicalIF":8.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CoreSense: Social Commonsense Knowledge-Aware Context Refinement for Conversational Recommender System CoreSense：会话推荐系统的社会常识知识感知上下文优化

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-01-30 DOI: 10.1109/TKDE.2025.3536464

Hyeongjun Yang;Donghyun Kim;Gayeon Park;KyuHwan Yeom;Kyong-Ho Lee

{"title":"CoreSense: Social Commonsense Knowledge-Aware Context Refinement for Conversational Recommender System","authors":"Hyeongjun Yang;Donghyun Kim;Gayeon Park;KyuHwan Yeom;Kyong-Ho Lee","doi":"10.1109/TKDE.2025.3536464","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3536464","url":null,"abstract":"Unlike the traditional recommender systems that rely on historical data such as clicks or purchases, a conversational recommender system (CRS) aims to provide a personalized recommendation through a natural conversation. The conversational interaction facilitates capturing not only explicit preference from mentioned items but also implicit states, such as a user’s current situation and emotional states from a dialogue context. Nevertheless, existing CRSs fall short of fully exploiting a dialogue context since they primarily derive explicit user preferences from the items and item-attributes mentioned in a conversation. To address this limitation and attain a comprehensive understanding of a dialogue context, we propose <underline>CoreSense, a <underline>conversational <underline>recommender system enhanced with social common<underline>sense knowledge. In other words, CoreSense exploits the social commonsense knowledge graph ATOMIC to capture the user’s implicit states, such as a user’s current situation and emotional states, from a dialogue context. Thus, the social commonsense knowledge-augmented CRS can provide a more appropriate recommendation from a given dialogue context. Furthermore, we enhance the collaborative filtering effect by utilizing the user’s states inferred from commonsense knowledge as an improved criterion for retrieving other dialogues of similar interests. Extensive experiments on CRS benchmark datasets show that CoreSense provides human-like recommendations and responses based on inferred user states, achieving significant performance improvements.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1702-1713"},"PeriodicalIF":8.9,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0