Abstract
We investigate how to solve the cross-corpus news recommendation for unseen users in the future. This is a problem where traditional content-based recommendation techniques often fail. Luckily, in real-world recommendation services, some publisher (e.g., Daily news) may have accumulated a large corpus with lots of consumers which can be used for a newly deployed publisher (e.g., Political news). To take advantage of the existing corpus, we propose a transfer learning model (dubbed as TrNews) for news recommendation to transfer the knowledge from a source corpus to a target corpus. To tackle the heterogeneity of different user interests and of different word distributions across corpora, we design a translator-based transfer-learning strategy to learn a representation mapping between source and target corpora. The learned translator can be used to generate representations for unseen users in the future. We show through experiments on real-world datasets that TrNews is better than various baselines in terms of four metrics. We also show that our translator is effective among existing transfer strategies.- Anthology ID:
- 2021.eacl-main.62
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 734–744
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.62
- DOI:
- 10.18653/v1/2021.eacl-main.62
- Cite (ACL):
- Guangneng Hu and Qiang Yang. 2021. TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 734–744, Online. Association for Computational Linguistics.
- Cite (Informal):
- TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation (Hu & Yang, EACL 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.eacl-main.62.pdf
- Data
- MIND