Combining Influence and Sensitivity to Factorize Matrix for Multi-Context Recommendation

With the growing amount of information available online, context-aware recommender systems have emerged to improve the precision of recommendation. Matrix factorization models are the state-of-the-art in these systems, especially for multi-context recommendation. However, existing models ignored either context influence or entity sensitivity. That is, they assume that one entity (user or item) shares the same factors across different contexts, or one context shares the same influence across different entities. In fact, for one context (or entity), its influence (or sensitivity) may be different with the changes of entities (or contexts). In this paper, we present a matrix factorization model for multi-context recommendation (namely ISMF). Unlike traditional models, ISMF considers both context influence and entity sensitivity. Instead of enforcing the same factors for each entity, we detail the factors as entity-intrinsic factors and entity-specific factors to represent entity-itself and context influence respectively. Meanwhile, we use some parameters acting on these factors to represent entity sensitivity. Also a matrix factorization algorithm for ISMF is proposed. We iteratively determine the factors and relevant parameters to maintain the precision for recommendation. The experiments demonstrate the feasibility and effectiveness of our method.