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。
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 recommendation.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.
作者
吴静
谢辉
姜火文
WU Jing;XIE Hui;JIANG Huowen(School of Mathematics and Computer Science,Jiangxi Science and Technology Normal University,Nanchang 330038,China)
出处
《计算机科学与探索》
CSCD
北大核心
2022年第10期2249-2263,共15页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金(71561013,61762044)
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,JD18083,JC18109)
江西省教育厅科技计划项目(GJJ211116,GJJ170661)。
