Beyond Preferences: Enriching User Profiles for Effective E-commerce Recommendations

Jingyu Xu, Zhengwei Yang, Zheng Wang

Abstract: Recommender systems are essential in E-Commerce platforms, with recent advancements leveraging users’ historical records to extract multi-interests. However, beyond these records, user profiles contain semantic information that inherently shapes their interests. Existing works mainly overlook that a user’s interests have: group influence, multi-level preference, and time relevance. To this end, a novel Enhanced User Profile-based Multi-interest Model (E-UPMiM) for recommendation is proposed to integrate enhanced user profiles with social relationships to model users’ multi-interests effectively. We propose to extract user preferences with three components: 1) integrated input containing enhanced profiles with social relationships to meet users’ grouping needs; 2) a multi-interest extraction module to obtain complex interest representations; and 3) a time-aware ranking module to adjust the recommendations dynamically. Extensive experiments on three public datasets show that E-UPMiM significantly outperforms state-of-the-art recommendation models. Codes are publicly available at: https://github.com/KevinXu-01/E-UPMiM.

External IDs:dblp:conf/iscas/XuYW25