Lei Li, Hehuan Liu, Yaxin Zhou, ZhaoYang Gui, Xudong Weng, Yi Yuan, Zheng Wei, Zang Li. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

User preference prediction is a task of learning user interests through user-item interactions. Most existing studies capture user interests based on historical behaviors without considering specific scenario information. However, the users may have special interests in these specific scenarios and sometimes user historical behaviors are limited. In this paper, we propose a Meta-Learned Specific Scenario Interest Network (Meta-SSIN) to predict user preference of target item by capturing specific scenario interests. Meta-SSIN uses multiple independent meta-learning modules to model historical behaviors in each scenario. The independent module can capture special interests based on limited behaviors. Experimental results on three datasets show that Meta-SSIN outperforms compared state-of-the-art methods.