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
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 3778–3787
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.332
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.coling-1.332.pdf
- Data
- MIND