Emotion Detection Techniques for the Evaluation of Serendipitous Recommendations

Abstract

Recommender systems analyze a user’s past behavior, build a user profile that stores information about her interests, maybe find others who have a similar profile, and use that information to find potentially interesting items. The main limitation of this approach is that provided recommendations are accurate, because they match the user profile, but not useful as they fall within the existing range of user interests. This drawback is known as overspecialization. New methods are being developed to compute serendipitous recommendations, i.e. unexpected suggestions that stimulate the user curiosity toward potentially interesting items she might not have otherwise discovered. The evaluation of those methods is not simple: there is a level of emotional response associated with serendipitous recommendations that is difficult to measure. In this chapter, we discuss the role of emotions in recommender systems research, with focus on their exploitation as implicit feedback on suggested items. Furthermore, we describe a user study which assesses both the acceptance and the perception of serendipitous recommendations, through the administration of questionnaires and the analysis of users’ emotions. Facial expressions of users receiving recommendations are analyzed to evaluate whether they convey a mixture of emotions that helps to measure the perception of serendipity of recommendations. The results showed that positive emotions such as happiness and surprise are associated with serendipitous suggestions.