Abstract
The cross-domain recommender systems aim to alleviate the data sparsity problem in a target domain by transferring knowledge from a source domain. However, existing works ignore the latent information underlying the user-item interactions. In addition, they don’t explicitly model the intra-domain and cross-domain interactions. To address these concerns, we propose a novel model named cross-graph knowledge transfer network to improve the recommendation performance. To explicitly model intra-domain and cross-domain interactions, we utilize the graph structure to transfer knowledge across domains. Firstly, we design a neighbor sampling method to extract useful intra-domain and cross-domain interactions. Secondly, we aggregate multiple interactive information in each domain and generate intra-domain embeddings by using intra-domain attention mechanism. Thirdly, we fuse the information from two domains to generate effective user and item embeddings by using cross-domain attention mechanism. Finally, we feed user and item embeddings into the domain-specific prediction layers for personalized recommendation. We conduct experiments on real-world datasets. The results show that our model outperforms five state-of-art methods.