Predicting New Adopters via Socially-Aware Neural Graph Collaborative Filtering

被引：1

作者：

Tsai, Yu-Che ^{[1
,3
]}

Guan, Muzhi ^{[2
,3
]}

Li, Cheng-Te ^{[1
]}

Cha, Meeyoung ^{[3
,4
]}

Li, Yong ^{[2
]}

Wang, Yue ^{[2
]}

机构：

[1] Natl Cheng Kong Univ, Dept Stat, Tainan, Taiwan

[2] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China

[3] Inst for Basic Sci Korea, Data Sci Grp, Daejeon, South Korea

[4] Korea Adv Inst Sci & Technol, Sch Comp, Daejeon, South Korea

来源：

COMPUTATIONAL DATA AND SOCIAL NETWORKS | 2019年 / 11917卷

基金：

新加坡国家研究基金会; 中国国家自然科学基金;

关键词：

Graph convolutional network; Collaborative filtering; Representation learning;

D O I：

10.1007/978-3-030-34980-6_18

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We predict new adopters of specific items by proposing SNGCF, a socially-aware neural graph collaborative filtering model. This model uses information about social influence and item adoptions; then it learns the representation of user-item relationships via a graph convolutional network. Experiments show that social influence is essential for adopter prediction. S-NGCF outperforms the prediction of new adopters compared to state-of-the-art methods by 18%.

引用

页码：155 / 162

页数：8

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