TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation

Guangneng Hu, Qiang Yang


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.62.pdf
Data
MIND