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Deep Transfer Collaborative Filtering with Geometric Structure Preservation for Cross-Domain Recommendation

Collaborative filtering (CF) is one of the most effective approaches for recommender systems by exploiting user-item behavior interactions. However, in real applications, the rating matrix is usually sparse, causing a poor performance. Numerous CF methods tend to incorporate the side information for the enrichment of priors. Cross-domain recommendation is an alternative to alleviate these two problems by referring to the knowledge of relevant domains. Due to the sparsity of the ratings and side information, the resulting latent factors might not be effective as expected. In this paper, we incorporate both the side information and deep knowledge transfer in CF models. A general architecture of deep transfer collaborative filtering with geometry preservation (DTCFGP) is proposed by integrating cross-domain collective matrix factorization, deep feature learning and the graph modeling. We exhibit a instantiations of our architecture by employing non-negative matrix tri-factorization and stacked denoising autoencoder (SDAE) in both source and target domains, where the common latent factors are taken as a bridge between domains due to its across-domain stability and data geometric structure is evaluated by using a nearest neighbor graph modeling. Extensive experiments on various real-world datasets demonstrate the effectiveness of our proposed approach in comparison to state-of-the-art approaches.