基于双向解耦知识迁移的跨域推荐算法OA

Cross-domain recommendation(CDR)has been proved to be effective to address the long-standing data sparsity and cold-start problems in traditional recommender systems.However,existing methods transfer knowledge at a coarse-grained level,ignoring multiple latent factors behind interactions.To this end,we propose a cross-domain recommendation model named D3-CDR,which enables dual knowledge transfer between domains.Specifically,we design a dual disentangled knowledge transfer layer that consists of several disentangled knowledge transfer(DKT)blocks.In each DKT block,we generate disentangled representations by projecting interaction representations into multiple latent semantic spaces.The attentive knowledge transfer mechanism distills useful knowledge generating cross-domain knowledge representations.The adaptive knowledge fusion mechanism further fuses within-domain knowledge with cross-domain knowledge adaptively.Extensive experiments on two real-world datasets demonstrate the effectiveness of our proposed D3CDR model.