A User-State Based Interest Transfer Network for Cross-Domain Recommendation

Pingjun Pan, Jialu Wang, Tingting Zhou, Wenyu Yang, Hongxiang Chen

Abstract: Cross-domain recommendation (CDR) has emerged as a promising approach to improve click-through rate (CTR) in the target domain by effectively transferring user interests from the source domain. However, existing methods either use a uniform interest transfer function or focus on user-level personalized transfer functions, neglecting the fact that the transition of user states in the target domain also influence the interests in the source domain. To address this issue, we present User-State based Interest Transfer network (USIT), a novel method that takes into account the user state evolution. USIT contains two main components: a User-State Transition module (UST) and a State-Level Interests Transfer module (SLIT). UST models the evolution of user states by predicting the next state in the target domain. As the user's state evolves, SLIT adaptively weights the interests by interest-level mask attention in the source domain. Extensive offline experiments and online A/B tests demonstrate that our proposed USIT method significantly outperforms current state-of-the-art models in CDR scenarios. Currently, we have deployed it on NetEase Cloud Music, affecting millions of users.