Sliced Wasserstein based Canonical Correlation Analysis for Cross-Domain Recommendation

Abstract

• A cross-domain recommendation model based on Sliced Wasserstein autoencoder is proposed. • An improved cross-domain transformation loss of orthogonal transformation is proposed. • Rigorous experiments were carried out to demonstrate the efficiency of the model. To solve the problem of data sparsity and cold start, the cross-domain recommendation is a promising research direction in the recommender system . The goal of cross-domain recommendation is to transfer learned knowledge from the source domain to the target domain by different means to improve the performance of the recommendation. But most approaches face the distribution misalignment . In this paper, we propose a joint learning cross-domain recommendation model that can extract domain-specific and common features simultaneously, and only use the implicit feedback data of users without additional auxiliary information. To the best of our knowledge, it is the first attempt to combine the sliced Wasserstein distance and canonical correlation analysis for the cross-domain recommendation scenario. Our one intuition is to reduce the reconstruction error caused by the variational inference based autoencoder model by the optimal transportation theory. Another attempt is to improve the correlation between domains by combining the idea of the canonical correlation analysis. With rigorous experiments, we empirically demonstrated that our model can achieve better performance compared with the state-of-the-art methods.