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In this presentation, I briefly discuss the use of automatically extracted visual features of videos in the context of recommender systems that brings some novel contributions in the domain of video recommendations. The proposed content-based recommender system encompasses a technique to automatically analyze video contents and to extract a set of representative stylistic features (lighting, color, and motion) grounded on existing approaches of Applied Media Theory.
Proposed recommender can be used in combination with more traditional content-based recommendation techniques that exploit explicit content features associated to video files, in order to improve the accuracy of recommendations. Proposed recommender can also be used alone, to address the problem originated from video files that have no meta-data, a typical situation of popular movie-sharing websites (e.g., YouTube) where every day hundred millions of hours of videos are uploaded by users and may contain no associated information. As they lack explicit content, these items cannot be considered for recommendation purposes by conventional content-based techniques even when they could be relevant for the user.