MVL: Multi-View Learning for News Recommendation

被引：25

作者：

Santosh, T. Y. S. S. ^{[1
]}

Saha, Avirup ^{[1
]}

Ganguly, Niloy ^{[1
]}

机构：

[1] IIT Kharagpur, Kharagpur, W Bengal, India

来源：

PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20) | 2020年

关键词：

news recommendation; graph view; multi-view learning;

D O I：

10.1145/3397271.3401294

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose a Multi-View Learning (MVL) framework for news recommendation which uses both the content view and the user-news interaction graph view. In the content view, we use a news encoder to learn news representations from different information like titles, bodies and categories. We obtain representation of user from his/her browsed news conditioned on the candidate news article to be recommended. In the graph-view, we propose to use a graph neural network to capture the user-news, user-user and news-news relatedness in the user-news bipartite graphs by modeling the interactions between different users and news. In addition, we propose to incorporate attention mechanism into the graph neural network to model the importance of these interactions for more informative representation learning of user and news. Experiments on a real world dataset validate the effectiveness of MVL.

引用

页码：1873 / 1876

页数：4

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