Paper Info
Title
AMM: Attentive Multi-field Matching for News Recommendation
Abstract
ABSTRACTPersonalized news recommendation is a critical technology to help users find interested news, and how to precisely match users' interests and candidate news lies in the core of news recommendation. Existing studies generally learn user's interest vector by aggregating his/her browsed news and then match it with the candidate news vector, which may lose the textual semantic matching signals for recommendation. In this paper, we propose an Attentive Multi-field Matching (AMM) framework for news recommendation which captures the semantic matching representations between each browsed news and candidate news, and then aggregates them as final user-news matching signal. In addition, our method incorporates multi-field information and designs a within-field and cross-field matching mechanism, which leverages complementary information from different fields (e.g., titles, abstracts and bodies) and obtain the multi-field matching representations. To achieve a comprehensive semantic understanding, we employ the most popular language model BERT to learn the matching representation of each browsed-candidate news pair, and incorporate the attention mechanism in aggregating procedure to characterize the importance of each matching representation for the final user-news matching signal. Experiments on the real world datasets validate the effectiveness of AMM.
Year
DOI
Venue
2021
10.1145/3404835.3463232
Research and Development in Information Retrieval
Keywords
DocType
Citations 
news recommendation, multi-field, semantic matching
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Qi Zhang110.69
Qinglin Jia211.03
Chuyuan Wang300.68
Jingjie Li410.69
Zhaowei Wang551.76
Xiuqiang He631239.21