One of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For example, in the popular movie-sharing websites, such as Youtube, everyday, hundred millions of hours of videos are uploaded and big portion of these videos may not contain any meta-data, to be used by the system to generate recommendations.
In this paper, we address this problem by proposing a method, that is based on automatic analysis of the video content in order to extract a number representative low-level visual features. Such features are then used to generate personalized content-based recommendations. Our evaluation shows that our proposed method can outperform the baselines, by producing more relevant recommendations. Hence, a set low-level features extracted automatically can be more descriptive and informative of the video content than a set of high-level expert annotated features.