图神经网络推荐研究进展  

Research advances in graph neural network recommendation

作　　者：吴国栋 查志康[2] 涂立静 陶鸿[2] 宋福根 WU Guodong;ZHA Zhikang;TU Lijing;TAO Hong;SONG Fugeng(School of Business and Management,Donghua University,Shanghai 200051,China;School of Information and Computer,Anhui Agricultural University,Hefei 230036,China)

机构地区：[1]东华大学管理学院,上海200051 [2]安徽农业大学信息与计算机学院,安徽合肥230036

出　　处：《智能系统学报》2020年第1期14-24,共11页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金资助项目(31671589);安徽省自然科学研究重点项目(KJ2017A152,KJ2019A0211).

摘　　要：图神经网络(graph neural network,GNN)具有从图的领域对数据进行特征提取和表示的优势,近年来成为人工智能研究的热点,图神经网络推荐也是推荐系统研究的一个新方向。本文对GNN模型进行深入研究的基础上,分析了GNN推荐过程,并从无向单元图推荐、无向二元图推荐、无向多元图推荐3个方面详细讨论了现有GNN推荐研究取得的主要进展及不足,阐明了现有GNN推荐研究中存在的主要难点,最后提出了GNN上下文推荐、GNN跨领域推荐、GNN群组推荐、GNN推荐的可解释性等未来GNN推荐的研究方向。Graph neural network(GNN)has the advantage of feature extraction and representation of data from the field of Graph.In recent years,it has become a hotspot of artificial intelligence research,and the recommendation of Graph Neural Network is also a new direction of recommendation system research.Based on the in-depth study of GNN model,this paper analyzes the GNN recommendation process,and discusses in detail the main progress and deficiencies of GNN recommendation studies from three aspects:undirected unit graph recommendation,undirected binary graph recommendation and undirected multivariate graph recommendation.The main difficulties in existing GNN recommendation studies are clarified,and the research directions of GNN recommendation in the future,including GNN contextual recommendation,GNN cross-domain recommendation,GNN group recommendation,and GNN recommendation’s interpretability,and so on,are pointed out in the end.

关 键 词：图神经网络 推荐系统 深度学习 实体联系 社交关系 协同过滤 无向图 有向图 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]