Cross domain recommendation based on multi-type media fusion

Due to the scarcity of user interest information in the target domain, recommender systems generally suffer from the sparsity problem. To alleviate this limitation, one natural way is to transfer user interests in other domains to the target domain. However, objects in different domains may be in different media types, which make it very difficult to find the correlations between them. In this paper, we propose a Bayesian hierarchical approach based on Latent Dirichlet Allocation (LDA) to transfer user interests cross domains or media. We model documents (corresponding to media objects) from different domains and user interests in a common topic space, and learn topic distributions for documents and user interests together. Specifically, to learn the model, we combine multi-type media information: media descriptions, user-generated text data and ratings. With this model, recommendation can be done in multiple ways, via predicting ratings, comparing topic distributions of documents and user interests directly and so on. Experiments on two real world datasets demonstrate that our proposed method is effective in addressing the sparsity problem by transferring user interests cross domains.