Zhenyu Li, Rong Gu, Gaogang Xie, Measuring and Enhancing the Social Connectivity of UGC Video Systems: a Case Study of YouKu, IEEE/ACM IWQoS 2011.

Abstract

The social connections among users in UGC video systems have significant impacts on the systems. The goal of this paper is to study the social connectivity of such systems by measuring YouKu, the most popular UGC video system in China. We have collected 627 thousand user profiles, 3 million social connections and 13.6 million videos' information. The analysis results have shown that the social connectivity is extremely weak and there are a considerable fraction of friend pairs sharing common semantic interests. These facts motivate us to enhance the social connectivity by recommending semantically relevant users as friends. We thus propose a friend recommendation algorithm which locates potential friends quickly and accurately through the links to related videos, a unique feature of YouKu and similar sites. The algorithm is distributed since only local information is required. We apply the algorithm on our data set of YouKu and evaluate it through one-hop video search. The social connectivity is greatly enhanced and the number of matched videos on one-hop friends is increased by four times. To the best of our knowledge, this work is the first to identify the semantic relevance between friend pairs in UGC video systems and to study the friend recommendation.

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