Accuracy meets Diversity in a News Recommender System

Shaina Raza, Syed Raza Bashir, Usman Naseem


Abstract
News recommender systems face certain challenges. These challenges arise due to evolving users’ preferences over dynamically created news articles. The diversity is necessary for a news recommender system to expose users to a variety of information. We propose a deep neural network based on a two-tower architecture that learns news representation through a news item tower and users’ representations through a query tower. We customize an augmented vector for each query and news item to introduce information interaction between the two towers. We introduce diversity in the proposed architecture by considering a category loss function that aligns items’ representation of uneven news categories. Experimental results on two news datasets reveal that our proposed architecture is more effective compared to the state-of-the-art methods and achieves a balance between accuracy and diversity.
Anthology ID:
2022.coling-1.332
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3778–3787
Language:
URL:
https://aclanthology.org/2022.coling-1.332
DOI:
Bibkey:
Cite (ACL):
Shaina Raza, Syed Raza Bashir, and Usman Naseem. 2022. Accuracy meets Diversity in a News Recommender System. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3778–3787, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Accuracy meets Diversity in a News Recommender System (Raza et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.332.pdf
Data
MIND