Abstract
In this paper we survey the work on the usage of personality and emotions in recommender systems. Recommender systems are designed to support humans making better decisions. It has been shown that personality and emotions account for the variance in human decision making. We present various models and acquisition methods for emotions and personality. Furthermore, we showcase examples of effective exploitation of personality and emotions in RS. We present in more details an example of the usage of emotions as implicit feedback for serendipitous recommendations. emotions personality decision making recommender systems.