ICAMF: Improved Context-Aware Matrix Factorization for Collaborative Filtering

Abstract

Context-aware recommender system (CARS) can provide more accurate rating predictions and more relevant recommendations by taking into account the contextual in-formation. Yet the state-of-the-art context-aware matrix factorization approaches only consider the influence of con-textual information on item bias. Tensor factorization based Multiverse Recommendation deals with the contextual in-formation by incorporating user-item-context interaction into recommendation model. However, all of these approaches cannot fully capture the influence of contextual information on the rating. In this paper, we propose two improved context-aware matrix factorization approaches to fully capture the influence of contextual information on the rating. Both of the baseline predictors (user bias and item bias) and user-item-context interaction are fully concerned. Experimental results on three semi-synthetic datasets and one real world dataset show that the two proposed approaches outperform Multiverse Recommendation and the state-of-the-art context-aware matrix factorization methods in prediction performance.