Multi-info Fusion Based Video Recommendation System

Abstract The great progress in recommendation system help users discover more interesting items that satisfy their appetites. Considering the video recommendation is an increasing popular sub-field of recommendation, but the traditional recommendation techniques such as Collaborative Filtering and Content-based model simply exploit one information source that limits its performance. In this paper, we proposed a Multi-info fusion based recommendation system which integrates several different information sources to comprehensively model the similarity between videos. The information sources including the common user-item rating data and video’s textual content that consists of video’s genres and textual description. Experimental results on a public dataset show that the proposed system is of high quality and achieves significant improvements over the traditional Collaborative Filtering techniques.