Contextual trace-based video recommendations

Raafat Zarka, Amélie Cordier, Elöd Egyed-Zsigmond, Alain Mille

Abstract: People like creating their own videos by mixing various contents. Many applications allow us to generate video clips by merging different media like videos clips, photos, text and sounds. Some of these applications enable us to combine online content with our own resources. Given the large amount of content available, the problem is to quickly find content that truly meet our needs. This is when recommender systems come in. In this paper, we propose an approach for contextual video recommendations based on a Trace-Based Reasoning approach.