Enhancing recommended video lists for Youtube-like social media

Abstract

Youtube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Facebook-like online social networks (OSNs), have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSSes are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users' viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends are more likely to have common interests and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for VSS video recommendation. An auto-encoder model is developed to learn the social similarity of different videos in terms of their sharing in OSN. We therefore propose a similarity-based strategy to enhance recommended video lists for VSSes. Evaluation results demonstrate that this strategy can remarkably improve the precision in VSSes, as compared to state-of-the-art strategies without social information.