Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
Hanyu Li, Weizhi Ma, Peijie Sun, Jiayu Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Min Zhang, Shaoping Ma
Abstract
As recommender systems become pervasive in various scenarios, cross-domain recommenders (CDR) are proposed to enhance the performance of one target domain with data from other related source domains. However, irrelevant information from the source domain may instead degrade target domain performance, which is known as the negative transfer problem. Most existing efforts to tackle this issue primarily focus on designing adaptive representations for overlapped users. Whereas, these methods rely on the learned representations of the model, lacking explicit constraints to filter irrelevant source-domain collaborative information for the target domain, which limits their cross-domain transfer capability.