PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity

Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang


Abstract
Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. However, these methods usually have difficulties in making accurate recommendations to cold-start users, and tend to recommend similar news with those users have read. In general, popular news usually contain important information and can attract users with different interests. Besides, they are usually diverse in content and topic. Thus, in this paper we propose to incorporate news popularity information to alleviate the cold-start and diversity problems for personalized news recommendation. In our method, the ranking score for recommending a candidate news to a target user is the combination of a personalized matching score and a news popularity score. The former is used to capture the personalized user interest in news. The latter is used to measure time-aware popularity of candidate news, which is predicted based on news content, recency, and real-time CTR using a unified framework. Besides, we propose a popularity-aware user encoder to eliminate the popularity bias in user behaviors for accurate interest modeling. Experiments on two real-world datasets show our method can effectively improve the accuracy and diversity for news recommendation.
Anthology ID:
2021.acl-long.424
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5457–5467
Language:
URL:
https://aclanthology.org/2021.acl-long.424
DOI:
10.18653/v1/2021.acl-long.424
Bibkey:
Cite (ACL):
Tao Qi, Fangzhao Wu, Chuhan Wu, and Yongfeng Huang. 2021. PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5457–5467, Online. Association for Computational Linguistics.
Cite (Informal):
PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity (Qi et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.424.pdf
Video:
 https://aclanthology.org/2021.acl-long.424.mp4