Enhancing video recommendation for YouTube-like social media

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 with common interest. 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. Through a long-term measurement, we show that friends have higher common interest and their sharing behaviors provide guidance for video recommendation. In this thesis, we take a first step toward learning OSN video sharing patterns for video recommendation. An auto-encoder model is developed to learn the social similarity of different videos. We therefore propose a similarity-based strategy to enhance video recommendation. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.