Rating Matrix Pre-Padding for Video Recommendation

Abstract

The personalized video recommendation system provides users with great convenience while surfing in the video websites. Among many algorithms adopted by recommendation system, the collaborative filtering algorithm is the most widely used and has achieved great success in practical applications, however, the recommended performance suffers from the problem of data sparsity severely. We propose a model that adopts Doc2Vec to deal with video's text information and integrates genre information into rating matrix pre-padding to reduce the sparsity of ratings. The experimental results show that pre-padding ratings is of high quality and the algorithms based on collaborative filtering achieve better performance on the padded datasets.