Personalized News Recommendation with Knowledge-aware Interactive Matching

被引：45
|
作者：
Qi, Tao [1 ,2 ]
Wu, Fangzhao [3 ]
Wu, Chuhan [1 ,2 ]
Huang, Yongfeng [1 ,2 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Tsinghua Univ, BNRist, Beijing 100084, Peoples R China
[3] Microsoft Res Asia, Beijing 100080, Peoples R China
来源：
SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL | 2021年
基金：
中国国家自然科学基金;
关键词：
News Recommendation; Interactive Matching; Single-Tower;
D O I：
10.1145/3404835.3462861
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The most important task in personalized news recommendation is accurate matching between candidate news and user interest. Most of existing news recommendation methods model candidate news from its textual content and user interest from their clicked news in an independent way. However, a news article may cover multiple aspects and entities, and a user usually has different kinds of interest. Independent modeling of candidate news and user interest may lead to inferior matching between news and users. In this paper, we propose a knowledge-aware interactive matching method for news recommendation. Our method interactively models candidate news and user interest to facilitate their accurate matching. We design a knowledge-aware news co-encoder to interactively learn representations for both clicked news and candidate news by capturing their relatedness in both semantic and entities with the help of knowledge graphs. We also design a user-news co-encoder to learn candidate news-aware user interest representation and useraware candidate news representation for better interest matching. Experiments on two real-world datasets validate that our method can effectively improve the performance of news recommendation.
引用
收藏
页码：61 / 70
页数：10
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