The cold start problem describes the initial phase of a collaborative recommender where the quality of recommendation is low due to an insufficient number of ratings. Overcoming this is crucial because the system’s adoption will be impeded by low recommendation quality. In this paper, we propose capturing context via computer vision to improve recommender systems in the cold start phase. Computer vision algorithms can derive stereotypes such as gender or age, but also the user’s emotions without explicit interaction. We present an approach based on the statistical framework of bandit algorithms to incorporate stereotypic information and affective reactions into the recommendation. In a preliminary evaluation in a lab study with 21 participants, we already observe an improvement of the number of positive ratings. Furthermore, we report additional findings of experimenting with affective computing for recommender systems.
Full paper available on CEUR-WS.