NDLI: Measuring and enhancing the social connectivity of UGC video systems: A case study of YouKu

Content Provider	IEEE Xplore Digital Library
Author	Zhenyu Li Rong Gu Gaogang Xie
Copyright Year	2011
Description	Author affiliation: Institute of Computing Technology, Chinese Academy of Sciences (Zhenyu Li; Rong Gu; Gaogang Xie)
Abstract	The social connections among users have significant impacts on UGC video 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 proportion of friend pairs sharing common semantic interests. These facts motivate us to enhance the 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. We apply the algorithm on our dataset of YouKu and evaluate it through one-hop video search. The social connectivity is greatly enhanced and the number of matched videos on friends is greatly increased. To the best of our knowledge, this work is the first to identify the semantic relevance of friend pairs in UGC video systems and to study the friend recommendation.
Starting Page	1
Ending Page	9
File Size	320704
Page Count	9
File Format	PDF
ISBN	9781457701047
ISSN	1548615X
e-ISBN	9781457701030
e-ISBN	9781457701023
DOI	10.1109/IWQOS.2011.5931346
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semantics YouTube Algorithm design and analysis Pollution measurement Servers Correlation
Content Type	Text
Resource Type	Article

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