Recommender Systems supporting Decision Making through Analysis of User Emotions and Personality

The influence of emotions in decision making is a popular research topic in psychology and cognitive studies. A person facing a choosing problem has to consider different solutions and take a decision. During this process several elements influence the reasoning, some of them are rational, others are irrational, such as emotions. Recommender Systems can be used to support decision making by narrowing the space of options. Typically they do not consider irrational elements during the computational process, but recent studies show that accuracy of suggestions improves whether user’s emotional state is included in the recommendation process. In this paper we propose the idea of defining a framework for an Emotion-Aware Recommender System. The user emotions will be formalized in an affective user profile which can act as an emotional computational model. The Recommender System will use the affective profile integrated with case base reasoning to compute recommendations.