Cross Domain Collaborative Filtering by Integrating User Latent Vectors of Auxiliary Domains

Abstract

Cross-Domain Collaborative Filtering solves the sparsity problem by transferring rating knowledge across multiple domains. However, how to transfer knowledge from auxiliary domains is nontrivial. In this paper, we propose a model-based CDCF algorithm by Integrating User Latent Vectors of auxiliary domains (CDCFIULV) from the perspective of classification. For a user-item interaction in the target domain, we first use the trivial location information as the feature vector, and use the rating information as the label. Thus we can convert the recommendation problem into a classification problem. However, such a two-dimensional feature vector is not sufficient to discriminate the different rating classes. Hence, we require some other features for the classification problem with the help of the rating information from the auxiliary domains.

In this paper, we assume the auxiliary domains contain dense rating data and share the same aligned users with the target domain. In this scenario, we employ UV decomposition model to obtain the user latent vectors from the auxiliary domains. We expand the trivial location feature vector in the target domain with the obtained user latent vectors from all the auxiliary domains. Thus we can effectively add features for the classification problem. Finally, we can train a classifier for this classification problem and predict the missing ratings for the recommender system. Hence the hidden knowledge in the auxiliary domains can be transferred to the target domain effectively via the user latent vectors. A major advantage of the CDCFIULV model over previous collective matrix factorization or tensor factorization models is that our model can adaptively select significant features during the training process. However, the previous collective matrix factorization or tensor factorization models need to adjust the weights of the auxiliary domains according to the similarities between the auxiliary domains and the target domain. We conduct extensive experiments to show that the proposed algorithm is more effective than many state-of-the-art single domain and cross domain CF methods.