Usage of affective computing in recommender systems
نویسندگان
چکیده
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.
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