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Online video recommendation in sharing community

Zhou, Xiangmin, Chen, Lei, Zhang, Yanchun, Cao, Longbing, Huang, Guangyan and Wang, Chen 2015, Online video recommendation in sharing community, in SIGMOD 2015 : Proceedings of the ACM International Conference on Management of Data, [The Conference], [Melbourne, Vic.], pp. 1645-1656, doi: 10.1145/2723372.2749444.

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Title Online video recommendation in sharing community
Author(s) Zhou, Xiangmin
Chen, Lei
Zhang, Yanchun
Cao, Longbing
Huang, Guangyan
Wang, Chen
Conference name Management of Data. Conference (2015 : Melbourne, Victoria)
Conference location Melbourne, Victoria
Conference dates 31 May. - 4 Jun. 2015
Title of proceedings SIGMOD 2015 : Proceedings of the ACM International Conference on Management of Data
Publication date 2015
Start page 1645
End page 1656
Total pages 12
Publisher [The Conference]
Place of publication [Melbourne, Vic.]
Summary The creation of sharing communities has resulted in the astonishing increasing of digital videos, and their wide applications in the domains such as entertainment, online news broadcasting etc. The improvement of these applications relies on effective solutions for social user access to video data. This fact has driven the recent research interest in social recommendation in shared communities. Although certain effort has been put into video recommendation in shared communities, the contextual information on social users has not been well exploited for effective recommendation. In this paper, we propose an approach based on the content and social information of videos for the recommendation in sharing communities. Specifically, we first exploit a robust video cuboid signature together with the Earth Mover's Distance to capture the content relevance of videos. Then, we propose to identify the social relevance of clips using the set of users belonging to a video. We fuse the content relevance and social relevance to identify the relevant videos for recommendation. Following that, we propose a novel scheme called sub-community-based approximation together with a hash-based optimization for improving the efficiency of our solution. Finally, we propose an algorithm for efficiently maintaining the social updates in dynamic shared communities. The extensive experiments are conducted to prove the high effectiveness and efficiency of our proposed video recommendation approach.
ISBN 9781450327589
ISSN 0730-8078
Language eng
DOI 10.1145/2723372.2749444
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, The Conference
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081983

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