Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (10): 2249-2263.DOI: 10.3778/j.issn.1673-9418.2203004

• Surveys and Frontiers • Previous Articles     Next Articles

Survey of Graph Neural Network in Recommendation System

WU Jing, XIE Hui+(), JIANG Huowen   

  1. School of Mathematics and Computer Science, Jiangxi Science and Technology Normal University, Nanchang 330038, China
  • Received:2022-03-01 Revised:2022-06-07 Online:2022-10-01 Published:2022-06-15
  • About author:WU Jing, born in 1997, M.S. candidate, student member of CCF. Her research interests include re-commendation system and graph neural networks.
    XIE Hui, born in 1978, Ph.D., associate profes-sor, member of CCF. His research interests in-clude data mining and intelligent recommen-dation.
    JIANG Huowen, born in 1974, Ph.D., profes-sor, member of CCF. His research interests inclu-de privacy preservation and computer education.
  • Supported by:
    National Natural Science Foundation of China(71561013);National Natural Science Foundation of China(61762044);Social Science Planning Projects in Jiangxi Province(20TQ04);Fund of Humanities and Social Sciences in Universities of Jiangxi Province(JC17221);Fund of Humanities and Social Sciences in Universities of Jiangxi Province(JD18083);Fund of Humanities and Social Sciences in Universities of Jiangxi Province(JC18109);Project of Science and Technology Plan by Education Department of Jiangxi Province(GJJ211116);Project of Science and Technology Plan by Education Department of Jiangxi Province(GJJ170661)

图神经网络推荐系统综述

吴静, 谢辉+(), 姜火文   

  1. 江西科技师范大学 数学与计算机科学学院,南昌 330038
  • 通讯作者: + E-mail: huixie@aliyun.com
  • 作者简介:吴静(1997—),女,江西九江人,硕士研究生,CCF学生会员,主要研究方向为推荐系统、图神经网络。
    谢辉(1978—),男,河南信阳人,博士,副教授,CCF会员,主要研究方向为数据挖掘、智能推荐。
    姜火文(1974—),男,江西进贤人,博士,教授,CCF会员,主要研究方向为隐私保护、计算机教育。
  • 基金资助:
    国家自然科学基金(71561013);国家自然科学基金(61762044);江西省社会科学研究规划项目(20TQ04);江西省高校人文社会科学研究项目(JC17221);江西省高校人文社会科学研究项目(JD18083);江西省高校人文社会科学研究项目(JC18109);江西省教育厅科技计划项目(GJJ211116);江西省教育厅科技计划项目(GJJ170661)

Abstract:

Recommendation system (RS) was introduced because of a lot of information. Due to the diversity, complexity, and sparseness of data, traditional recommendation system can not solve the current problem well. Graph neural network (GNN) can extract and represent the features from edges and nodes data in the graphs and has inherent advantages in processing the graphs structure data, so it flourishes in recommendation system. This paper sorts out the main references of graph neural network in recommendation system in recent years, focuses on the two perspectives of method and problem, and systematically reviews graph neural network in recommendation system. Firstly, from the method level, five graph neural networks of the recommendation system are elaborated, including the graph convolutional network in the recommendation system, graph attention network in the recommendation system, graph autoencoder in the recommendation system, graph generation network in the recommendation system and graph spatial-temporal network in the recommendation system. Secondly, from the perspective of problem similarity, six major problem types are summarized: sequence recommendation, social recommendation, cross-domain recommendation, multi-behavior recommendation, bundle recommendation, and session-based recommen-dation. Finally, based on the analysis and summary of the existing methods, this paper points out the main difficu-lties in the current research on graph neural network in recommendation system, proposes the corresponding issues that can be investigated, and looks forward to the future research directions on this topic.

Key words: graph neural network (GNN), recommendation system (RS), graph convolution network (GCN)

摘要:

推荐系统(RS)因信息冗杂繁多而诞生。由于数据形式的多样化、复杂化以及数据信息量稀疏性,传统的推荐系统已经不能很好地解决目前的问题。图神经网络(GNN)能从图中对边和节点数据进行特征提取和表示,对处理图结构数据具有先天优势,因此在推荐系统中蓬勃发展。将近年的主要研究成果进行了梳理并加以总结,着重从方法、问题两个角度出发,系统性地综述了图神经网络推荐系统。首先,从方法层面阐述了图卷积网络推荐系统、图注意力网络推荐系统、图自动编码器推荐系统、图生成网络推荐系统、图时空网络推荐系统等五大类的图神经网络推荐系统;接着,从问题相似性出发,归纳出序列推荐问题、社交推荐问题、跨域推荐问题、多行为推荐问题、捆绑推荐问题以及基于会话推荐问题等六大类问题;最后,在对已有方法分析和总结的基础上,指出了目前图神经网络推荐系统研究面临的难点,提出相应的研究问题以及未来研究的方向。

关键词: 图神经网络(GNN), 推荐系统(RS), 图卷积网络(GCN)

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