A cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains

Cross-domain collaborative filtering, which transfers rating knowledge across multiple domains, has become a new way to effectively alleviate the sparsity problem in recommender systems. Different auxiliary domains are generally different in the importance to the target domain, which is hard to evaluate using previous approaches. Besides, most recommender systems only take advantage of information from user- or item-side auxiliary domains. To...