Graph neural news recommendation based on multi-view representation learning

被引：0

作者：

Li, Xiaohong ^{[1
]}

Li, Ruihong ^{[1
]}

Peng, Qixuan ^{[1
]}

Yao, Jin ^{[1
]}

机构：

[1] Northwest Normal Univ, Coll Comp Sci & Engn, Lanzhou 730070, Peoples R China

来源：

JOURNAL OF SUPERCOMPUTING | 2024年 / 80卷 / 10期

关键词：

Graph neural network; Multi-head self-attention; User modeling; News recommendation; News modeling;

D O I：

10.1007/s11227-024-06025-9

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Accurate news representation is of crucial importance in personalized news recommendation. Most of existing news recommendation model lack comprehensiveness because they do not consider the higher-order structure between user-news interactions, relevance between user clicks on news. In this paper, we propose graph neural news recommendation based on multi-view representation learning which encodes high-order connections into the representation of news through information propagation along the graph. For news representations, we learn click news and candidate news content information embedding from various news attributes. And then combine obtained structure-based representations with representations from news content. Besides, we adopt a candidate-aware attention network to weight clicked news based on their relevance with candidate news to learn candidate-aware user interest representation for better matching with candidate news. The performance of the model has been improved in common evaluation metric. Extensive experiments on benchmark datasets show that our approach can effectively improve performance in news recommendation.

引用

页码：14470 / 14488

页数：19

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