Graph Enhanced Representation Learning for News Recommendation

被引：101

作者：

Ge, Suyu ^{[1
]}

Wu, Chuhan ^{[1
]}

Wu, Fangzhao ^{[2
]}

Qi, Tao ^{[1
]}

Huang, Yongfeng ^{[1
]}

机构：

[1] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Beijing, Peoples R China

[2] Microsoft Res Asia, Beijing, Peoples R China

来源：

WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020) | 2020年

基金：

中国国家自然科学基金;

关键词：

News Recommendation; Transformer; Graph Attention Network;

D O I：

10.1145/3366423.3380050

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the explosion of online news, personalized news recommendation becomes increasingly important for online news platforms to help their users find interesting information. Existing news recommendation methods achieve personalization by building accurate news representations from news content and user representations from their direct interactions with news (e.g., click), while ignoring the high-order relatedness between users and news. Here we propose a news recommendation method which can enhance the representation learning of users and news by modeling their relatedness in a graph setting. In our method, users and news are both viewed as nodes in a bipartite graph constructed from historical user click behaviors. For news representations, a transformer architecture is first exploited to build news semantic representations. Then we combine it with the information from neighbor news in the graph via a graph attention network. For user representations, we not only represent users from their historically clicked news, but also attentively incorporate the representations of their neighbor users in the graph. Improved performances on a large-scale real-world dataset validate the effectiveness of our proposed method.

引用

页码：2863 / 2869

页数：7

共 38 条

[1]

An MX, 2019, 57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), P336

[2]

[Anonymous], 2015, P 9 ACM C REC SYST

[3]

[Anonymous], 2016, P 1 WORKSH DEEP LEAR, DOI 10.1145/2988450.2988454

[4]

[Anonymous], 2011, ECIR

[5] Merging trust in collaborative filtering to alleviate data sparsity and cold start [J].

Guo, Guibing ;

Zhang, Jie ;

Thalmann, Daniel .

KNOWLEDGE-BASED SYSTEMS, 2014, 57 :57-68

[6]

Hamilton W.L., 2017, ARXIV170905584

[7]

Huang PS, 2013, PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), P2333

[8]

Jones Karen Sparck, 2004, J DOCUMENTATION

[9]

Kingma DP, 2014, ADV NEUR IN, V27

[10] GroupLens: Applying collaborative filtering to Usenet news [J].

Konstan, JA ;

Miller, BN ;

Maltz, D ;

Herlocker, JL ;

Gordon, LR ;

Riedl, J .

COMMUNICATIONS OF THE ACM, 1997, 40 (03) :77-87

← 1 2 3 4 →