Usage of affective computing in recommender systems
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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.