{"title":"Graph and Sequential Neural Networks in Session-based Recommendation: A Survey","authors":"Zihao Li, Chao Yang, Yakun Chen, Xianzhi Wang, Hongxu Chen, Guandong Xu, Lina Yao, Michael Sheng","doi":"10.1145/3696413","DOIUrl":null,"url":null,"abstract":"Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims to provide a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"39 1","pages":""},"PeriodicalIF":23.8000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3696413","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims to provide a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.
近年来，推荐系统（RS）在缓解信息过载问题方面取得了巨大成功。作为推荐系统的一种新模式，基于会话的推荐（SR）专门针对用户的短期偏好，旨在根据持续的互动提供更动态、更及时的推荐。本调查报告全面概述了最近有关会话推荐的研究成果。首先，我们明确了 SR 的关键定义，并将 SR 的特点与其他推荐任务进行了比较。然后，我们将现有方法归纳为两类：基于序列神经网络的方法和基于图神经网络（GNN）的方法。我们还进一步介绍了相关框架和技术细节。最后，我们讨论了 SR 所面临的挑战以及该领域新的研究方向。
期刊介绍：
ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods.
ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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