Cross-domain recommendation for cold-start users via neighborhood based feature mapping

Abstract

Traditional Collaborative Filtering (CF) models mainly focus on predicting a user’s preference to the items in a single domain such as the movie domain or the music domain. A major challenge for such models is the data sparsity problem, and especially, CF cannot make accurate predictions for the cold-start users who have no ratings at all. Although Cross-Domain Collaborative Filtering (CDCF) is proposed for effectively transferring users’ rating preference across different domains, it is still difficult for existing CDCF models to tackle the cold-start users in the target domain due to the extreme data sparsity. In this paper, we propose a Cross-Domain Latent Feature Mapping (CDLFM) model for cold-start users in the target domain. Firstly, the user rating behavior is taken into consideration in the matrix factorization for alleviating the data sparsity. Secondly, neighborhood based latent feature mapping is proposed to transfer the latent features of a cold-start user from the auxiliary domain to the target domain. Extensive experiments on two real datasets extracted from Amazon transaction data demonstrate the superiority of our proposed model against other state-of-the-art methods.