An online video recommendation framework using rich information

Abstract

Automatic video recommendation is involved in an attempt to tackle the information-overload problem, aiming to present the personalized video list to the user. This paper presents a novel approach to improve the accuracy of the video recommendation by combining the content-based filtering (CBF) method and the collaborative filtering (CF) method. Multimodal information is utilized to calculate the similarity among different videos to overcome the sparseness problem by CF method. We conduct experiments on a dataset of more than 11,000 videos and the results demonstrate the feasibility and effectiveness of our approach. © 2011 ACM.