SoRank: incorporating social information into learning to rank models for recommendation

Yao, Weilong, He, Jing, Huang, Guangyan and Zhang, Yanchun 2014, SoRank: incorporating social information into learning to rank models for recommendation, in WWW 2014 : Proceedings of the 23rd International Conference on World Wide Web, Association for Computing Machinery, New York, N.Y., pp. 409-410, doi: 10.1145/2567948.2577333.

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Title SoRank: incorporating social information into learning to rank models for recommendation
Author(s) Yao, Weilong
He, Jing
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
Zhang, Yanchun
Conference name World Wide Web. International Conference (23rd : 2014 : Seoul, Korea)
Conference location Seoul, Korea
Conference dates 2014/04/07 - 2014/04/11
Title of proceedings WWW 2014 : Proceedings of the 23rd International Conference on World Wide Web
Publication date 2014
Conference series International World Wide Web Conference
Start page 409
End page 410
Total pages 2
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Keyword(s) learning to rank
maxtrix factorization
social relation
Summary Most existing learning to rank based recommendation methods only use user-item preferences to rank items, while neglecting social relations among users. In this paper, we propose a novel, effective and efficient model, SoRank, by integrating social information among users into listwise ranking model to improve quality of ranked list of items. In addition, with linear complexity to the number of observed ratings, SoRank is able to scale to very large dataset. Experimental results on publicly available dataset demonstrate the effectiveness of SoRank.
ISBN 9781450327459
Language eng
DOI 10.1145/2567948.2577333
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30093147

Document type: Conference Paper
Collection: School of Information Technology
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