MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation
Senrong Xu, Liangyue Li, Yuan Yao, Zulong Chen, Han Wu, Quan Lu, Hanghang Tong · 2023
Personalized recommendation has been instrumental in many real applications. Despite the great progress, the underlying multi-scenario characteristics (e.g., users may behave differently under different scenarios) are largely ignored by existing recommender systems. Intuitively, modeling different scenarios properly could significantly improve the recommendation accuracy, and some existing work has explored this direction. However, these work assumes the scenarios are explicitly given, and thus becomes less effective when such information is unavailable. To complicate things further, proper scenario modeling from data is challenging and the recommendation models may easily overfit to some scenarios. In this paper, we propose a multi-scenario learning framework, MUSENET, for personalized recommendation. The key idea of MUSENET is to learn multiple implicit scenarios from the user behaviors, with a careful design inspired by the causal interpretation of recommender systems to avoid the overfitting issue. Additionally, since users' repeat consumptions account for a large part of the user behavior data on many e-commerce platforms, a repeat-aware mechanism is integrated to handle users' repurchase intentions within each scenario. Comprehensive experimental results on both industrial and public datasets demonstrate the effectiveness of the proposed approach compared with the state-of-the-art methods.