A Unified Video Recommendation by Cross-Network User Modeling

Abstract

Online video sharing sites are increasingly encouraging their users to connect to the social network venues such as Facebook and Twitter, with goals to boost user interaction and better disseminate the high-quality video content. This in turn provides huge possibilities to conduct cross-network collaboration for personalized video recommendation. However, very few efforts have been devoted to leveraging users’ social media profiles in the auxiliary network to capture and personalize their video preferences, so as to recommend videos of interest. In this article, we propose a unified YouTube video recommendation solution by transferring and integrating users’ rich social and content information in Twitter network. While general recommender systems often suffer from typical problems like cold-start and data sparsity, our proposed recommendation solution is able to effectively learn from users’ abundant auxiliary information on Twitter for enhanced user modeling and well address the typical problems in a unified framework. In this framework, two stages are mainly involved: (1) auxiliary-network data transfer, where user preferences are transferred from an auxiliary network by learning cross-network knowledge associations; and (2) cross-network data integration, where transferred user preferences are integrated with the observed behaviors on a target network in an adaptive fashion. Experimental results show that the proposed cross-network collaborative solution achieves superior performance not only in terms of accuracy, but also in improving the diversity and novelty of the recommended videos.