Adapt to Emotional Reactions in Context-aware Personalization

Context-aware recommender systems (CARS) have been developed to adapt to users’ preferences in different contextual situations. Users’ emotions have been demonstrated as one of effective context information in recommender systems. However, there are no work exploring the effect of emotional reactions (or expressions) in the recommendation process. In this paper, we assume that users may give similar ratings even if they present different emotional reactions or expressions on the movies. We further model the traits of emotional reactions and incorporate them into context-aware matrix factorization as regularization terms. Our experimental results based on the LDOS-CoMoDa movie data set validate our assumptions and prove that it is useful to take emotional reactions into consideration in context-aware recommendations.