Multi-Domain Collaborative Filtering

Collaborative filtering is an effective recommen- dation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big chal- lenge in using collaborative filtering methods is the data sparsity problem which often arises be- cause each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploit- ing the relationships between domains. We re- fer to it as a multi-domain collaborative filter- ing (MCF) problem. To solve the MCF prob- lem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to cor- rect their biases. Experiments conducted on sev- eral real-world applications demonstrate the ef- fectiveness of our methods when compared with some representative methods.