Social Connections in User-Generated Content Video Systems: Analysis and Recommendation

摘要

User-generated content (UGC) video systems by definition heavily depend on the input of their community of users and their social interactions for video diffusion and opinion sharing. Nevertheless, we show in this paper, through measurement and analysis of YouKu, the most popular UGC video system in China, that the social connectivity of its users is very low. These observations are consistent with what was reported about YouTube in previous works. As a UGC system can achieve a larger audience through improved connectivity, our findings motivate us to propose a mean to enhance the users' connectivity by taking benefit of friend recommendation. To this end, we assess two similarity metrics based on users' interests that are derived from their uploads and favorites tagging of videos, to evaluate the interest similarity between friends. The results consistently show that friends share to a great extent common interests. Two friend recommendation algorithms are then proposed. The algorithms use public information provided by users to suggest potential friends with similar interests as measured by the similarity metrics. Experiments on our gathered YouKu dataset demonstrate that the social connectivity can be greatly enhanced by our friend proposition set and that users can access a larger set of interesting videos through the recommendations.