[1]吴国栋,查志康,涂立静,等.图神经网络推荐研究进展[J].智能系统学报,2020,15(1):14-24.[doi:10.11992/tis.201908034]
 WU Guodong,ZHA Zhikang,TU Lijing,et al.Research advances in graph neural network recommendation[J].CAAI Transactions on Intelligent Systems,2020,15(1):14-24.[doi:10.11992/tis.201908034]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年1期
页码:
14-24
栏目:
综述
出版日期:
2020-01-01

文章信息/Info

Title:
Research advances in graph neural network recommendation
作者:
吴国栋12 查志康2 涂立静2 陶鸿2 宋福根1
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
Author(s):
WU Guodong12 ZHA Zhikang2 TU Lijing2 TAO Hong2 SONG Fugeng1
1. School of Business and Management, Donghua University, Shanghai 200051, China;
2. School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
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
Keywords:
graph neural networkrecommendation systemdeep learningentity relationssociety relationcollaborative filteringundirected graphdirected graph
分类号:
TP301
DOI:
10.11992/tis.201908034
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。
Abstract:
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.

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备注/Memo

备注/Memo:
收稿日期:2019-08-30。
基金项目: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)
作者简介:吴国栋,副教授,中国计算机学会会员,主要研究方向为深度学习、推荐系统。主持安徽省自然科学研究重点项目1项、一般项目1项、安徽省科技攻关重点项目1项。发表学术论文30余篇;查志康,硕士研究生,主要研究方向为推荐系统;涂立静,讲师,主要研究方向为人工智能、机器学习。主持安徽省自然科学研究一般项目1项、安徽农业大学青年基金项目1项。发表学术论文10余篇
通讯作者:吴国栋.E-mail:gdwu1120@qq.com
更新日期/Last Update: 1900-01-01