Two-way Cross-domain Recommendation with Central Social Influence

Shuyi Zhang, Jing Li, Jun Chang, Mingfeng Wang, Kai Zhu

Abstract: As an effective method to solve the cold start problem of recommender systems, cross-domain recommendation has received more and more attention and research. Currently, most cross-domain recommendation models rely on the unidirectional knowledge transfer between the same users in the source and target domains. The rich information in the source domain is transferred to the target domain with sparse information to achieve better recommendation effect. However, the performance of cross-domain recommendation heavily depends on the number of overlapping active users, and these models cannot fully utilize the useful knowledge behind active users in a single domain. This limitation makes it difficult for the model to achieve ideal results in real-world scenarios. To solve the above problems and optimize cross-domain recommendation, we propose a Two-way Cross-domain Recommendation with Central Social Influence(CST-CDR) and the concept of super-user. Through the idea of clustering, user circles are formed centering on active super-users, so the potential common preferences of single-field active users, dual-field active users, and super-users are fully explored, alleviating the dependence of the model on overlapping and active users. At the same time, the cross-domain recommendation is extended to realize two-way information migration, so that users who are active in only one domain can have a preliminary preference judgment. Finally, the effectiveness of the proposed method is demonstrated on two real datasets.