Towards Better Content Visibility in Video Recommender Systems

Current recommender systems based on filtering techniques implement a rather limited model for video content visibility. Most of these systems fall short to provide visual precursor to the user and concentrate only on making more accurate predictions; however, a few of them that focus their attention to the aspect of multimedia (video) item visibility do so in a limited scope. In this paper, we address this problem and propose to augment the existing recommender systems with a dynamic user-based scheme to provide users with superior, high-quality recommendation formulation and customized visibility of the recommended item. The domain of content visibility is dynamically crafted using the existing recommender system algorithm.