MINER: Multi-Interest Matching Network for News Recommendation

Jian Li, Jieming Zhu, Qiwei Bi, Guohao Cai, Lifeng Shang, Zhenhua Dong, Xin Jiang, Qun Liu


Abstract
Personalized news recommendation is an essential technique to help users find interested news. Accurately matching user’s interests and candidate news is the key to news recommendation. Most existing methods learn a single user embedding from user’s historical behaviors to represent the reading interest. However, user interest is usually diverse and may not be adequately modeled by a single user embedding. In this paper, we propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest. We further propose a disagreement regularization to make the learned interests vectors more diverse. Moreover, we design a category-aware attention weighting strategy that incorporates the news category information as explicit interest signals into the attention mechanism. Extensive experiments on the MIND news recommendation benchmark demonstrate that our approach significantly outperforms existing state-of-the-art methods.
Anthology ID:
2022.findings-acl.29
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
343–352
Language:
URL:
https://aclanthology.org/2022.findings-acl.29
DOI:
10.18653/v1/2022.findings-acl.29
Bibkey:
Cite (ACL):
Jian Li, Jieming Zhu, Qiwei Bi, Guohao Cai, Lifeng Shang, Zhenhua Dong, Xin Jiang, and Qun Liu. 2022. MINER: Multi-Interest Matching Network for News Recommendation. In Findings of the Association for Computational Linguistics: ACL 2022, pages 343–352, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
MINER: Multi-Interest Matching Network for News Recommendation (Li et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.29.pdf
Data
MIND