PPT: A Minor Language News Recommendation Model via Cross-Lingual Preference Pattern Transfer

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 wellestablished 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 fewshot 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.