@inproceedings{zhang-chen-2025-ppt,
title = "{PPT}: A Minor Language News Recommendation Model via Cross-Lingual Preference Pattern Transfer",
author = "Zhang, Yiyang and
Chen, Nan",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/landing_page/2025.acl-long.526/",
pages = "10728--10745",
ISBN = "979-8-89176-251-0",
abstract = "Rich user-item interactions are essential for building reliable recommender systems, as they reflect user preference patterns. However, minor language news recommendation platforms suffer from limited interactions due to a small user base. A natural solution is to apply well-established English recommender systems to minor language news recommendation, but the linguistic gap can lead to inaccurate modeling of minor language news content. Therefore, enabling few-shot minor language news recommender systems to capture both content information and preference patterns remains a challenge. Based on the observation that preference patterns are similar across languages, we propose a minor language news recommendation model by cross-lingual preference pattern transfer, named PPT. Our model adopts the widely used two-tower architecture and employs the large language model as the backbone of the news encoder. Through cross-lingual alignment, the strong English capability of the news encoder is extended to minor languages, thus enhancing news content representations. Additionally, through cross-lingual news augmentation, PPT simulates interactions of minor language news in the English domain, which facilitates the transfer of preference patterns from the many-shot English domain to the few-shot minor language domain. Extensive experiments on two real-world datasets across 15 minor languages demonstrate the superiority and generalization of our proposed PPT in addressing minor language news recommendation."
}
Markdown (Informal)
[PPT: A Minor Language News Recommendation Model via Cross-Lingual Preference Pattern Transfer](https://preview.aclanthology.org/landing_page/2025.acl-long.526/) (Zhang & Chen, ACL 2025)
ACL