Multi-graph Convolutional Feature Transfer for Cross-domain Recommendation

Cross-domain recommendation(CDR) is an effective method to alleviate the data sparsity problem in the recommendation system. How to learn common and domain-specific feature embeddings of users and items is a challenge especially where the two domains do not completely share users. Based on the superiority of graph structure feature learning, we propose a method of multi-graph convolutional feature transfer for cross-domain recommendation (MGCDR). For the common feature learning, MGCDR applies the feature similarity method to construct the residual network with the overlapping users, and then pre-training the feature embedding of the overlapping users. For the specific feature learning, MGCDR transfers the pre-trained embedding to initialize the spcific-domain graph, and obtain specific features through domain-specific users information propagation. Moreover, the attention mechanism is adopted to adaptively fuse the feature embedding. MGCDR is a dual-target recommendation model that fully exploits cross-domain mutual enhancement and joint training. Experiments on two pairs of real-world cross-domain datasets show the effectiveness of MGCDR.