Pub Date : 2025-01-15DOI: 10.1109/TBDATA.2025.3526356
{"title":"2024 Reviewers List*","authors":"","doi":"10.1109/TBDATA.2025.3526356","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3526356","url":null,"abstract":"","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"310-313"},"PeriodicalIF":7.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993640","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}
Pub Date : 2024-11-22DOI: 10.1109/TBDATA.2024.3505055
Guilherme Ramos;Ludovico Boratto;Mirko Marras
Online marketplaces often collect products to sell from several other platforms (and sellers) and produce a unique ranking/score of these products to users. Keeping as private the user preferences provided in each (individual) platform is a need and a challenge at the same time. We are currently used to rating items in the marketplace itself which, in turn, can produce more effective rankings. Hence, the shaping of an effective item ranking would require a sharing of the user ratings between the individual platforms and the marketplace, thus impacting users’ privacy. In this paper, we propose the initial steps towards a change of paradigm, where the ratings are kept as private in each platform. Under this paradigm, each platform produces its rankings, then aggregated by the marketplace, in a federated fashion. To ensure that the marketplace’s rankings maintain their effectiveness, we exploit the concept of reputation of the individual platform, so that the final marketplace ranking is weighted by the reputation of each platform providing its ranking. Experiments on three datasets, covering different use cases, show that our approach can produce effective rankings, improving robustness to attacks, while keeping user preference data private within each seller platform.
{"title":"Robust Privacy-Preserving Federated Item Ranking in Online Marketplaces: Exploiting Platform Reputation for Effective Aggregation","authors":"Guilherme Ramos;Ludovico Boratto;Mirko Marras","doi":"10.1109/TBDATA.2024.3505055","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3505055","url":null,"abstract":"Online marketplaces often collect products to sell from several other platforms (and sellers) and produce a unique ranking/score of these products to users. Keeping as private the user preferences provided in each (individual) platform is a need and a challenge at the same time. We are currently used to rating items in the marketplace itself which, in turn, can produce more effective rankings. Hence, the shaping of an effective item ranking would require a sharing of the user ratings between the individual platforms and the marketplace, thus impacting users’ privacy. In this paper, we propose the initial steps towards a change of paradigm, where the ratings are kept as private in each platform. Under this paradigm, each platform produces its rankings, then aggregated by the marketplace, in a federated fashion. To ensure that the marketplace’s rankings maintain their effectiveness, we exploit the concept of <italic>reputation of the individual platform</i>, so that the final marketplace ranking is weighted by the reputation of each platform providing its ranking. Experiments on three datasets, covering different use cases, show that our approach can produce effective rankings, improving robustness to attacks, while keeping user preference data private within each seller platform.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"303-309"},"PeriodicalIF":7.5,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993638","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}
Pub Date : 2024-11-12DOI: 10.1109/TBDATA.2024.3452328
Senzhang Wang;Changdong Wang;Di Jin;Shirui Pan;Philip S. Yu
{"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}
Pub Date : 2024-11-06DOI: 10.1109/TBDATA.2024.3489412
Yuxin Guo;Deyu Bo;Cheng Yang;Zhiyuan Lu;Zhongjian Zhang;Jixi Liu;Yufei Peng;Chuan Shi
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently, instead of designing more complex neural architectures as model-centric approaches, the attention of AI community has shifted to data-centric ones, which focuses on better processing data to strengthen the ability of neural models. Graph learning, which operates on ubiquitous topological data, also plays an important role in the era of deep learning. In this survey, we comprehensively review graph learning approaches from the data-centric perspective, and aim to answer three crucial questions: (1) when to modify graph data, (2) what part of the graph data needs modification to unlock the potential of various graph models, and (3) how to safeguard graph models from problematic data influence. Accordingly, we propose a novel taxonomy based on the stages in the graph learning pipeline, and highlight the processing methods for different data structures in the graph data, i.e., topology, feature and label. Furthermore, we analyze some potential problems embedded in graph data and discuss how to solve them in a data-centric manner. Finally, we provide some promising future directions for data-centric graph learning.
人工智能（AI）的历史见证了高质量数据对各种深度学习模型（如ImageNet for AlexNet和ResNet）的重大影响。最近，人工智能界的注意力从设计更复杂的神经架构作为以模型为中心的方法，转向以数据为中心的方法，即更好地处理数据以增强神经模型的能力。在本调查中，我们从数据中心的角度全面回顾了图学习方法，并旨在回答三个关键问题：(1)何时修改图数据，(2)需要修改图数据的哪一部分以释放各种图模型的潜力，以及(3)如何保护图模型免受问题数据的影响。因此，我们提出了一种基于图学习管道阶段的新分类方法，并重点介绍了图数据中不同数据结构（拓扑、特征和标签）的处理方法。此外，我们还分析了图数据中的一些潜在问题，并讨论了如何以数据为中心的方式解决这些问题。最后，我们为以数据为中心的图学习提供了一些有希望的未来方向。
{"title":"Data-Centric Graph Learning: A Survey","authors":"Yuxin Guo;Deyu Bo;Cheng Yang;Zhiyuan Lu;Zhongjian Zhang;Jixi Liu;Yufei Peng;Chuan Shi","doi":"10.1109/TBDATA.2024.3489412","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489412","url":null,"abstract":"The history of artificial intelligence (AI) has witnessed the significant impact of high-quality data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently, instead of designing more complex neural architectures as model-centric approaches, the attention of AI community has shifted to data-centric ones, which focuses on better processing data to strengthen the ability of neural models. Graph learning, which operates on ubiquitous topological data, also plays an important role in the era of deep learning. In this survey, we comprehensively review graph learning approaches from the data-centric perspective, and aim to answer three crucial questions: <italic>(1) when to modify graph data</i>, <italic>(2) what part of the graph data needs modification</i> to unlock the potential of various graph models, and <italic>(3) how to safeguard graph models</i> from problematic data influence. Accordingly, we propose a novel taxonomy based on the stages in the graph learning pipeline, and highlight the processing methods for different data structures in the graph data, i.e., topology, feature and label. Furthermore, we analyze some potential problems embedded in graph data and discuss how to solve them in a data-centric manner. Finally, we provide some promising future directions for data-centric graph learning.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"1-20"},"PeriodicalIF":7.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993790","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}
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 R eliable D ata Augmented C ontrastive Learning Rec ommender (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.
{"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}
Pub Date : 2024-09-03DOI: 10.1109/TBDATA.2024.3453757
Ming Li;Zhao Li;Changqin Huang;Yunliang Jiang;Xindong Wu
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 EduGraph , a novel framework developed specifically for MOOC course recommendation systems. EduGraph 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, EduGraph 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 EduGraph 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 EduGraph 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.
{"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}
Pub Date : 2024-09-03DOI: 10.1109/TBDATA.2024.3453761
Dan Du;Pei-Yuan Lai;Yan-Fei Wang;De-Zhang Liao;Min Chen
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 A ttribute K nowledge G raph N eural N etworks (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 e xtended V ariational A uto- E ncoder (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.
{"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}
Pub Date : 2024-09-03DOI: 10.1109/TBDATA.2024.3453758
Ling Huang;Zhi-Yuan Li;Zhen-Yu He;Yuefang Gao
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 H igher-order S moothness enhanced G raph C ollaborative F iltering (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.
{"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}
Pub Date : 2024-09-03DOI: 10.1109/TBDATA.2024.3453765
Haibo Ye;Lijun Zhang;Yuan Yao;Sheng-Jun Huang
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 N eighborhood- i nformed Den oising 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.
{"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}
Pub Date : 2024-06-13DOI: 10.1109/TBDATA.2024.3414280
Xi Yang;Suining He;Kang G. Shin;Mahan Tabatabaie;Jing Dai
We propose an equity-aware GRAph-fusion differentiable Pooling neural network to accurately predict the spatio-temporal urban mobility (e.g., station-level bike usage in terms of departures and arrivals) with Equity (GRAPE). GRAPE consists of two independent hierarchical graph neural networks for two mobility systems—one as a target graph (i.e., a bike sharing system) and the other as an auxiliary graph (e.g., a taxi system). We have designed a convolutional fusion mechanism to jointly fuse the target and auxiliary graph embeddings and extract the shared spatial and temporal mobility patterns within the embeddings to enhance prediction accuracy. To further improve the equity of bike sharing systems for diverse communities, we focus on the bike resource allocation and model prediction performance, and propose to regularize the predicted bike resource as well as the accuracy across advantaged and disadvantaged communities, and thus mitigate the potential unfairness in the predicted bike sharing usage. Our evaluation of over 23 million bike rides and 100 million taxi trips in New York City and Chicago has demonstrated GRAPE to outperform all of the baseline approaches in terms of prediction accuracy (by 15.80% for NYC and 50.55% for Chicago on average) and social equity awareness (by 32.44% and 24.43% in terms of resource fairness for NYC and Chicago, and 13.36% and 16.52% in terms of performance fairness).
{"title":"Cross-Modality and Equity-Aware Graph Pooling Fusion: A Bike Mobility Prediction Study","authors":"Xi Yang;Suining He;Kang G. Shin;Mahan Tabatabaie;Jing Dai","doi":"10.1109/TBDATA.2024.3414280","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3414280","url":null,"abstract":"We propose an equity-aware <underline><i>GRA</i></u>ph-fusion differentiable <underline><i>P</i></u>ooling neural network to accurately predict the spatio-temporal urban mobility (e.g., station-level bike usage in terms of departures and arrivals) with <underline><i>E</i></u>quity (<monospace>GRAPE</monospace>). <monospace>GRAPE</monospace> consists of two independent hierarchical graph neural networks for two mobility systems—one as a target graph (i.e., a bike sharing system) and the other as an auxiliary graph (e.g., a taxi system). We have designed a convolutional fusion mechanism to jointly fuse the target and auxiliary graph embeddings and extract the shared spatial and temporal mobility patterns within the embeddings to enhance prediction accuracy. To further improve the equity of bike sharing systems for diverse communities, we focus on the bike resource allocation and model prediction performance, and propose to regularize the predicted bike resource as well as the accuracy across advantaged and disadvantaged communities, and thus mitigate the potential unfairness in the predicted bike sharing usage. Our evaluation of over 23 million bike rides and 100 million taxi trips in New York City and Chicago has demonstrated <monospace>GRAPE</monospace> to outperform all of the baseline approaches in terms of prediction accuracy (by 15.80% for NYC and 50.55% for Chicago on average) and social equity awareness (by 32.44% and 24.43% in terms of resource fairness for NYC and Chicago, and 13.36% and 16.52% in terms of performance fairness).","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 1","pages":"286-302"},"PeriodicalIF":7.5,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993639","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}
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