Tutorial on cross-domain recommender systems

Cross-domain recommender systems aim to generate or enhance personalized recommendations in a target domain by exploiting knowledge (mainly user preferences) from other source domains. This may beneficial for generating better recommendations, e.g. mitigating the cold-start and sparsity problems in a target domain, and enabling personalized cross-selling for items from multiple domains. In this tutorial, we formalize the cross-domain recommendation problem, categorize and survey state of the art cross-domain recommender systems, discuss related evaluation issues, and outline future research directions on the topic.