LSCD: Low-rank and sparse cross-domain recommendation

Due to the ability of addressing the data sparsity and cold-start problems, Cross-Domain Collaborative Filtering (CDCF) has received a significant amount of attention. Despite significant success, most of the existing CDCF algorithms assume that all the domains are correlated, which is however not always guaranteed in practice. In this paper, we propose a novel CDCF algorithm termed Low-rank and Sparse Cross-Domain (LSCD) recommendation algorithm. Different from most of the CDCF algorithms, LSCD extracts a user and an item latent feature matrix for each domain respectively, rather than tri-factorizing the rating matrix of each domain into three low dimensional matrices. In order to simultaneously improve the performance of recommendations among correlated domains by transferring knowledge and among uncorrelated domains by differentiating features in different domains, the features of users are separated into shared and domain-specific parts adaptively. Specifically, a low-rank matrix is used to capture the shared features of each user across different domains and a sparse matrix is used to characterize the discriminative features in each specific domain. Extensive experiments on two real-world datasets have been conducted to confirm that the proposed algorithm transfers knowledge in a better way to improve the quality of recommendation and outperforms state-of-the-art recommendation algorithms. (C) 2019 Elsevier B.V. All rights reserved.