Correlation-Based Context-aware Matrix Factorization

In contrast to traditional recommender systems, context-aware recommender systems (CARS) additionally take context into consideration and try to adapt their recommendations to users’ different contextual situations. Several contextual recommendation algorithms have been developed by incorporating context into recommenders in different ways. Most of those recommendation algorithms consider modeling contextual rating deviations but ignore the correlations among contexts. In this paper, we highlight the importance of contextual correlations, and build a correlation-based context-aware matrix factorization algorithm which demonstrates and further confirms the effectiveness of contextual correlations.