IEEE Transactions on Big Data最新文献

{"title":"Guest Editorial TBD Special Issue on Graph Machine Learning for Recommender Systems","authors":"Senzhang Wang;Changdong Wang;Di Jin;Shirui Pan;Philip S. Yu","doi":"10.1109/TBDATA.2024.3452328","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3452328","url":null,"abstract":"","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 6","pages":"682-682"},"PeriodicalIF":7.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable Data Augmented Contrastive Learning for Sequential Recommendation","authors":"Mankun Zhao;Aitong Sun;Jian Yu;Xuewei Li;Dongxiao He;Ruiguo Yu;Mei Yu","doi":"10.1109/TBDATA.2024.3453752","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3453752","url":null,"abstract":"Sequential recommendation aims to capture users’ dynamic preferences. Due to the limited information in the sequence and the uncertain user behavior, data sparsity has always been a key problem. Although data augmentation methods can alleviate this issue, unreliable data can affect the performance of such models. To solve the above problems, we propose a new framework, namely \u0000<bold>R</b>\u0000eliable \u0000<bold>D</b>\u0000ata Augmented \u0000<bold>C</b>\u0000ontrastive Learning \u0000<bold>Rec</b>\u0000ommender (RDCRec). Specifically, in order to generate more high-quality reliable items for data augmentation, we design a multi-attributes oriented sequence generator. It moves auxiliary information from the input layer to the attention layer for learning a better attention distribution. Then, we replace a percentage of items in the original sequence with reliable items generated by the generator as the augmented sequence, for creating a high-quality view for contrastive learning. In this way, RDCRec can extract more meaningful user patterns by using the self-supervised signals of the reliable items, thereby improving recommendation performance. Finally, we train a discriminator to identify unreplaced items in the augmented sequence thus we can update item embeddings selectively in order to increase the exposure of more reliable items and improve the accuracy of recommendation results. The discriminator, as an auxiliary model, is jointly trained with the generative task and the contrastive learning task. Large experiments on four popular datasets that are commonly used demonstrate the effectiveness of our new method for sequential recommendation.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 6","pages":"694-705"},"PeriodicalIF":7.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EduGraph: Learning Path-Based Hypergraph Neural Networks for MOOC Course Recommendation","authors":"Ming Li;Zhao Li;Changqin Huang;Yunliang Jiang;Xindong Wu","doi":"10.1109/TBDATA.2024.3453757","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3453757","url":null,"abstract":"In online learning, personalized course recommendations that align with learners’ preferences and future needs are essential. Thus, the development of efficient recommender systems is crucial to guide learners to appropriate courses. Graph learning in recommender systems has been extensively studied, yet many models focus on low-frequency information, underscoring similar learner preferences and overlooking high-frequency data that indicates varied learning trajectories. Furthermore, course co-occurrence and sequential relationships are often insufficiently investigated. In this paper, we introduce \u0000<monospace><b>EduGraph</b></monospace>\u0000, a novel framework developed specifically for MOOC course recommendation systems. \u0000<monospace><b>EduGraph</b></monospace>\u0000 is characterized by its incorporation of a learning path-based hypergraph, a unique perspective wherein learners are represented as hyperedges, and courses are delineated as vertices. The framework incorporates a framelet-based hypergraph convolution, integrating low-pass filters to highlight similarities and high-pass filters to underscore distinct learning paths among learners. Furthermore, \u0000<monospace><b>EduGraph</b></monospace>\u0000 features a dual hypergraph learning model, with channels designated for vertex and hyperedge encoding, fostering a collaborative information exchange that refines the learners’ preference embeddings. The empirical assessment of \u0000<monospace><b>EduGraph</b></monospace>\u0000 is conducted through a comprehensive comparison with many existing baselines, utilizing two distinct MOOC datasets. Our experimental studies not only emphasize the enhanced recommendation performance of \u0000<monospace><b>EduGraph</b></monospace>\u0000 but also elucidate the significant contributions of its individual components, such as the integration of low-pass and high-pass filters and the framelet-wise collaborative strategy that effectively bridges hyperedge-level and vertex-level representations, augmenting the overall efficacy of the course recommendation system.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 6","pages":"706-719"},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AKGNN: Attribute Knowledge Graph Neural Networks Recommendation for Corporate Volunteer Activities","authors":"Dan Du;Pei-Yuan Lai;Yan-Fei Wang;De-Zhang Liao;Min Chen","doi":"10.1109/TBDATA.2024.3453761","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3453761","url":null,"abstract":"Due to the collective decision-making nature of enterprises, the process of accepting recommendations is predominantly characterized by an analytical synthesis of objective requirements and cost-effectiveness, rather than being rooted in individual interests. This distinguishes enterprise recommendation scenarios from those tailored for individuals or groups formed by similar individuals, rendering traditional recommendation algorithms less applicable in the corporate context. To overcome the challenges, by taking the corporate volunteer as an example, which aims to recommend volunteer activities to enterprises, we propose a novel recommendation model called \u0000<bold>A</b>\u0000ttribute \u0000<bold>K</b>\u0000nowledge \u0000<bold>G</b>\u0000raph \u0000<bold>N</b>\u0000eural \u0000<bold>N</b>\u0000etworks (AKGNN). Specifically, a novel comprehensive attribute knowledge graph is constructed for enterprises and volunteer activities, based on which we obtain the feature representation. Then we utilize an \u0000<bold>e</b>\u0000xtended \u0000<bold>V</b>\u0000ariational \u0000<bold>A</b>\u0000uto-\u0000<bold>E</b>\u0000ncoder (eVAE) model to learn the preferences representation and then we utilize a GNN model to learn the comprehensive representation with representation of the similar nodes. Finally, all the comprehensive representations are input to the prediction layer. Extensive experiments have been conducted on real datasets, confirming the advantages of the AKGNN model. We delineate the challenges faced by recommendation algorithms in Business-to-Business (B2B) platforms and introduces a novel research approach utilizing attribute knowledge graphs.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 6","pages":"720-730"},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Higher-Order Smoothness Enhanced Graph Collaborative Filtering","authors":"Ling Huang;Zhi-Yuan Li;Zhen-Yu He;Yuefang Gao","doi":"10.1109/TBDATA.2024.3453758","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3453758","url":null,"abstract":"Graph Neural Networks (GNNs) based recommendations have shown significant performance improvement by explicitly modeling the user-item interactions as a bipartite graph. However, the existing GNNs-based recommendation methods suffer from the over-smoothing problem caused by utilizing the uniform distance of the reception field. To address this issue, we propose to explicitly incorporate the higher-order smoothness information into the node representation learning, and propose a new GNNs-based recommendation model named \u0000<underline>H</u>\u0000igher-order \u0000<underline>S</u>\u0000moothness enhanced \u0000<underline>G</u>\u0000raph \u0000<underline>C</u>\u0000ollaborative \u0000<underline>F</u>\u0000iltering (HS-GCF). The proposed model is mainly composed of two parts, namely lower-order module and higher-order module. The lower-order module guarantees that the lower-order smoothness is well obtained by using the user-item interactions. The higher-order module uses the latent group assumption to restrict too much noise introduced by the uniform distance property, which we call the higher-order smoothness information. Experiments are conducted on three real-world public datasets, and the experimental results show the performance improvements compared with several state-of-the-art methods and verify the importance of explicitly incorporating the higher-order smoothness information into the node representation learning.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 6","pages":"731-741"},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Denoised Graph Collaborative Filtering via Neighborhood Similarity and Dynamic Thresholding","authors":"Haibo Ye;Lijun Zhang;Yuan Yao;Sheng-Jun Huang","doi":"10.1109/TBDATA.2024.3453765","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3453765","url":null,"abstract":"Graph collaborative filtering (GCF) has achieved great success in recommender systems due to its ability in mining high-order collaborative signals from historical user-item interactions. However, GCF's performance could be severely affected by the intrinsic noise within the user-item interactions. To this end, several denoised GCF frameworks have been proposed, whose heart is to estimate and handle the reliability of existing interactions. However, most of them suffer from two limitations: 1) the reliability computation itself is noisy, and 2) the reliability threshold is difficult to determine. To address the two limitations, in this paper, we propose a new \u0000<underline>N</u>\u0000eighborhood-\u0000<underline>i</u>\u0000nformed \u0000<underline>Den</u>\u0000oising framework NiDen for GCF. Specifically, for an existing user-item interaction, NiDen first estimates its reliability by employing the neighborhood information of the user and the item, and then determines whether the interaction is noisy or not via a dynamic thresholding strategy. After that, NiDen mitigates the negative impact of noise by both structure denoising and sample re-weighting. We instantiate NiDen on two representative GCF models and conduct extensive experiments on four widely-used datasets. The results show that NiDen achieves the best performance compared to the existing denoising methods, especially on datasets with heavy noise.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 6","pages":"683-693"},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fast and Robust Attention-Free Heterogeneous Graph Convolutional Network","authors":"Yeyu Yan;Zhongying Zhao;Zhan Yang;Yanwei Yu;Chao Li","doi":"10.1109/TBDATA.2024.3375152","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3375152","url":null,"abstract":"Due to the widespread applications of heterogeneous graphs in the real world, heterogeneous graph neural networks (HGNNs) have developed rapidly and made a great success in recent years. To effectively capture the complex interactions in heterogeneous graphs, various attention mechanisms are widely used in designing HGNNs. However, the employment of these attention mechanisms brings two key problems: high computational complexity and poor robustness. To address these problems, we propose a \u0000<bold>Fast</b>\u0000 and \u0000<bold>Ro</b>\u0000bust attention-free \u0000<bold>H</b>\u0000eterogeneous \u0000<bold>G</b>\u0000raph \u0000<bold>C</b>\u0000onvolutional \u0000<bold>N</b>\u0000etwork (FastRo-HGCN) without any attention mechanisms. Specifically, we first construct virtual links based on the topology similarity and feature similarity of the nodes to strengthen the connections between the target nodes. Then, we design type normalization to aggregate and transfer the intra-type and inter-type node information. The above methods are used to reduce the interference of noisy information. Finally, we further enhance the robustness and relieve the negative effects of oversmoothing with the self-loops of nodes. Extensive experimental results on three real-world datasets fully demonstrate that the proposed FastRo-HGCN significantly outperforms the state-of-the-art models.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 5","pages":"669-681"},"PeriodicalIF":7.5,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAER: Fairness-Aware Event-Participant Recommendation in Event-Based Social Networks","authors":"Yuan Liang","doi":"10.1109/TBDATA.2024.3372409","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3372409","url":null,"abstract":"The \u0000<underline>e</u>\u0000vent-\u0000<underline>b</u>\u0000ased \u0000<underline>s</u>\u0000ocial \u0000<underline>n</u>\u0000etwork (EBSN) is a new type of social network that combines online and offline networks. In recent years, an important task in EBSN recommendation systems has been to design better and more reasonable recommendation algorithms to improve the accuracy of recommendation and enhance user satisfaction. However, the current research seldom considers how to coordinate fairness among individual users and reduce the impact of individual unreasonable feedback in group event recommendation. In addition, when considering the fairness to individuals, the accuracy of recommendation is not greatly improved by fully incorporating the key context information. To solve these problems, we propose a prefiltering algorithm to filter the candidate event set, a multidimensional context recommendation method to provide personalized event recommendations for each user in the group, and a group consensus function fusion strategy to fuse the recommendation results of the members of the group. To improve overall satisfaction with the recommendations, we propose a ranking adjustment strategy for the key context. Finally, we verify the effectiveness of our proposed algorithm on real data sets and find that FAER is superior to the latest algorithms in terms of global satisfaction, distance satisfaction and user fairness.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 5","pages":"655-668"},"PeriodicalIF":7.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An End-to-End Approach for Graph-Based Multi-View Data Clustering","authors":"Fadi Dornaika;Sally El Hajjar","doi":"10.1109/TBDATA.2024.3371357","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3371357","url":null,"abstract":"Clustering data from different sources or views is a key challenge in real-world applications. While traditional graph-based methods are effective at capturing data structures, they often require separate steps to estimate graphs of views or a consensus graph from the raw data. This reliance on intermediate steps can make these clustering methods susceptible to noisy graphs, which affects the overall performance of clustering. In response to this limitation, and with an emphasis on advocating end-to-end solutions for multi-view clustering, two comprehensive approaches are presented in this paper. Each approach starts from either the raw data or its kernelized features. The first proposal introduces a unified objective function that enables the simultaneous recovery of the graph for each view, the unified graph, the spectral projection matrices for all views, the soft cluster assignments, and the scores assigned to each view. The second proposal uses a global criterion that integrates regularization and constraints for the soft cluster assignment matrix based on the consensus graph matrix and the consensus data representation. Both proposed methods enable direct and straightforward clustering of the data without the need for additional steps. Extensive tests with various real-world image and text datasets confirm the superior performance of the two proposed methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 5","pages":"644-654"},"PeriodicalIF":7.5,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TgStore: An Efficient Storage System for Large Time-Evolving Graphs","authors":"Yongli Cheng;Yan Ma;Hong Jiang;Lingfang Zeng;Fang Wang;Xianghao Xu;Yuhang Wu","doi":"10.1109/TBDATA.2024.3366087","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3366087","url":null,"abstract":"Existing graph systems focus mainly on the execution efficiency of the graph analysis tasks, often ignoring the importance and efficiency of time-evolving graph storage. However, to effectively mine the potential application values, an efficient storage system is important for time-evolving graphs whose storage requirement scales with the increasing number of snapshots. Storage cost and snapshot access speed are the two most important performance indicators for a time-evolving graph storage system, which are challenging for designers of such systems because they are conflicting goals. In this article, we address these challenges by proposing an efficient storage scheme for the large time-evolving graphs. We first design a \u0000<italic>Snapshot-level Data Deduplication (SLDD)</i>\u0000 strategy to eliminate the large number of repeated vertices and edges among the snapshots, and then a \u0000<italic>Structure-Changing Graph Representation (SCGR)</i>\u0000 to significantly improve the snapshot access speed. We implement an efficient time-evolving graph storage system, TgStore, based on this scheme to effectively store large-scale time-evolving graphs, aiming to efficiently support the time-evolving graph analysis tasks. Experimental results show that TgStore can obtain a high compression ratio of 43.03:1 when storing 100 snapshots of Twitter, while with an average snapshot access speedup of 16×. Efficient storage scheme enables TgStore to efficiently support time-evolving graph algorithms. For example, when executing the Pagerank algorithm on the time-evolving graph of Twitter, TgStore outperforms Graphone, a state-of-the-art time-evolving graph storage system, by 15.9× in algorithm execution speed and 1.45× in memory usage.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 2","pages":"158-173"},"PeriodicalIF":7.2,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140123521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}