On video recommendation over social network

Abstract

Video recommendation is a hot research topic to help people access interesting videos. The existing video recommendation approaches include CBF, CF and HF. However, these approaches treat the relationships between all users as equal and neglect an important fact that the acquaintances or friends may be a more reliable source than strangers to recommend interesting videos. Thus, in this paper we propose a novel approach to improve the accuracy of video recommendation. For a given user, our approach calculates a recommendation score for each video candidate that composes of two parts: the interest degree of this video by the user's friends, and the relationship strengths between the user and his friends. The final recommended videos are ranked according to the accumulated recommendation scores from different recommenders. We conducted experiments with 45 participants and the results demonstrated the feasibility and effectiveness of our approach. © 2012 Springer-Verlag.