NPA: Neural News Recommendation with Personalized Attention

被引：232

作者：

Wu, Chuhan ^{[1
]}

Wu, Fangzhao ^{[2
]}

An, Mingxiao ^{[3
]}

Huang, Jianqiang ^{[4
]}

Huang, Yongfeng ^{[1
]}

Xie, Xing ^{[2
]}

机构：

[1] Tsinghua Univ, Beijing, Peoples R China

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

[3] USTC, Hefei, Anhui, Peoples R China

[4] Peking Univ, Beijing, Peoples R China

来源：

KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING | 2019年

基金：

中国国家自然科学基金;

关键词：

News Recommendation; Neural Network; Personalized Attention;

D O I：

10.1145/3292500.3330665

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word-and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word-and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation.

引用

页码：2576 / 2584

页数：9

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