Abstract
In this paper we present the results of three investigations of our broad research on the usage of affect and personality in recommender systems. We improved the accuracy of a content-based recommender system with the inclusion of affective parameters in user and item modeling. We improved the accuracy of a content filtering recommender system under the cold start conditions with the introduction of a personality-based user similarity measure. Furthermore we developed a system for implicit tagging of images with affective metadata.