Affective recommender systems: The role of emotions in recommender systems
A unifying framework is introduced that positions the research work, that has been done so far in a scattered manner, in a three stage model, in an attempt to improve the quality of recommender systems.
Abstract
Recommender systems have traditionally relied on data-centric descriptors for content and user modeling. In recent years we have witnessed an increasing number of attempts to use emotions in different ways to improve the quality of recommender systems. In this paper we introduce a unifying framework that positions the research work, that has been done so far in a scattered manner, in a three stage model. We provide examples of research that cover various aspects of the detection of emotions and the inclusion of emotions into recommender systems.