In this paper, we propose a novel Cross-Domain Collaborative Filtering (CDCF) algorithm termed Low-rank and Sparse Cross-Domain (LSCD) recommendation algorithm. Different from most of the CDCF algorithms which tri-factorize the rating matrix of each domain into three low dimensional matrices, LSCD extracts a user and an item latent feature matrix for each domain respectively. Besides, in order to improve the performance of recommendations among correlated domains by transferring knowledge and 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 feature subspace of users 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 the state-of-the-art recommendation algorithms.
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