{"title":"利用视图探索器改进图协同过滤,实现社交推荐","authors":"Yongrui Duan, Yijun Tu, Yusheng Lu, Xiaofeng Wang","doi":"10.1007/s10844-024-00865-w","DOIUrl":null,"url":null,"abstract":"<p>Social recommender systems (SRS) have garnered adequate attention due to the supplementary information provided by social network, which aids in making recommendations. However, social network information contains noise, which can be detrimental to recommendation performance. Current social recommendation models are deficient in feature validation and extraction of social data. To fill that gap, we propose a novel model called Social View Explorer Collaborative Filtering (SVE-CF) which aims to extract significant consistent signals from the noisy social network. First, SVE-CF correlates users’ social and interaction behaviors, creating follow, joint, and interaction views to represent all interaction patterns. Second, it samples unlabeled examples from users to assess consistency across the three views, assigning pseudo-labels as evidence of social homophily. Third, it selects top-k pseudo-labels to amplify significant consistent signals and minimize noise through tri-view joint learning. Extensive experiments are conducted to demonstrate the effectiveness of the proposed model over the commonly used state-of-the-art (SOTA) methods.</p>","PeriodicalId":56119,"journal":{"name":"Journal of Intelligent Information Systems","volume":"46 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving graph collaborative filtering with view explorer for social recommendation\",\"authors\":\"Yongrui Duan, Yijun Tu, Yusheng Lu, Xiaofeng Wang\",\"doi\":\"10.1007/s10844-024-00865-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Social recommender systems (SRS) have garnered adequate attention due to the supplementary information provided by social network, which aids in making recommendations. However, social network information contains noise, which can be detrimental to recommendation performance. Current social recommendation models are deficient in feature validation and extraction of social data. To fill that gap, we propose a novel model called Social View Explorer Collaborative Filtering (SVE-CF) which aims to extract significant consistent signals from the noisy social network. First, SVE-CF correlates users’ social and interaction behaviors, creating follow, joint, and interaction views to represent all interaction patterns. Second, it samples unlabeled examples from users to assess consistency across the three views, assigning pseudo-labels as evidence of social homophily. Third, it selects top-k pseudo-labels to amplify significant consistent signals and minimize noise through tri-view joint learning. Extensive experiments are conducted to demonstrate the effectiveness of the proposed model over the commonly used state-of-the-art (SOTA) methods.</p>\",\"PeriodicalId\":56119,\"journal\":{\"name\":\"Journal of Intelligent Information Systems\",\"volume\":\"46 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent Information Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10844-024-00865-w\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10844-024-00865-w","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
社交推荐系统(SRS)因社交网络提供的补充信息而受到广泛关注,这些信息有助于进行推荐。然而,社交网络信息包含噪音,可能会影响推荐性能。当前的社交推荐模型在特征验证和社交数据提取方面存在不足。为了填补这一空白,我们提出了一种名为 "社交观点探索者协同过滤(SVE-CF)"的新模型,旨在从嘈杂的社交网络中提取重要的一致信号。首先,SVE-CF 将用户的社交和互动行为关联起来,创建关注视图、联合视图和互动视图来代表所有互动模式。其次,SVE-CF 从用户中抽取未标记的示例来评估这三种视图的一致性,并分配伪标签作为社交亲缘关系的证据。第三,它通过三视图联合学习,选择前 k 个伪标签,以放大重要的一致信号,尽量减少噪音。我们进行了广泛的实验,以证明所提出的模型比常用的最先进(SOTA)方法更有效。
Improving graph collaborative filtering with view explorer for social recommendation
Social recommender systems (SRS) have garnered adequate attention due to the supplementary information provided by social network, which aids in making recommendations. However, social network information contains noise, which can be detrimental to recommendation performance. Current social recommendation models are deficient in feature validation and extraction of social data. To fill that gap, we propose a novel model called Social View Explorer Collaborative Filtering (SVE-CF) which aims to extract significant consistent signals from the noisy social network. First, SVE-CF correlates users’ social and interaction behaviors, creating follow, joint, and interaction views to represent all interaction patterns. Second, it samples unlabeled examples from users to assess consistency across the three views, assigning pseudo-labels as evidence of social homophily. Third, it selects top-k pseudo-labels to amplify significant consistent signals and minimize noise through tri-view joint learning. Extensive experiments are conducted to demonstrate the effectiveness of the proposed model over the commonly used state-of-the-art (SOTA) methods.
期刊介绍:
The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems.
These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to:
discover knowledge from large data collections,
provide cooperative support to users in complex query formulation and refinement,
access, retrieve, store and manage large collections of multimedia data and knowledge,
integrate information from multiple heterogeneous data and knowledge sources, and
reason about information under uncertain conditions.
Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces.
The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.