Multi-perspective social recommendation method with graph representation learning

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成果类型：

期刊论文

作者：

Liu, Hai;Zheng, Chao*;Li, Duantengchuan;Zhang, Zhaoli;Lin, Ke;...

通讯作者：

Zheng, Chao

作者机构：

[Xiong, Neal N.; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.

[Zheng, Chao; Shen, Xiaoxuan] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.

[Li, Duantengchuan] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.

[Lin, Ke] Harbin Inst Technol Shenzhen, Dept Control Sci & Engn, Shenzhen, Peoples R China.

[Wang, Jiazhang] Northwestern Univ, Evanston, IL USA.

通讯机构：

[Zheng, Chao] C

Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan, Peoples R China.

语种：

英文

关键词：

Diverse social relation;Graph convolutional network;Recommender system;Representation learning

期刊：

Neurocomputing

ISSN：

0925-2312

年：

2022

卷：

468

页码：

469-481

DOI：

10.1016/j.neucom.2021.10.050

基金类别：

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [62177018, 62177019, 62011530436, 62077020, 62005092, 61875068]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2020M682454]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [CCNU20ZT017, CCNU2020ZN008]

机构署名：

本校为第一且通讯机构

院系归属：

国家数字化学习工程技术研究中心

摘要：

Social recommender systems (SRS) aim to study how social relations influence users' choices and how to use them for better learning users embeddings. However, the diversity of social relationships, which is instructive to the propagation of social influence, has been rarely explored. In this paper, we propose a graph convolutional network based representation learning method, namely multi-perspective social recommendation (MPSR), to construct hierarchical user preferences and assign friends' influences with different levels of trust from varying perspectives. We further utilize the attributes ...

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Multi-perspective social recommendation method with graph representation learning

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