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Ordinal random fields for recommender systems

Liu, Shaowu, Tran, Truyen, Li, Gang and Jiang,Yuan 2014, Ordinal random fields for recommender systems, in ACML 2014: Proceedings of the Sixth Asian Conference on Machine Learning, JMLR Workshop and Conference Proceedings, [Nha Trang, Vietnam], pp. 283-298.

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Title Ordinal random fields for recommender systems
Author(s) Liu, Shaowu
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Jiang,Yuan
Conference name Asian Conference on Machine Learning (6th : 2014 : Nha Trang, Vietnam)
Conference location Nha Trang, Vietnam
Conference dates 2014/11/26 - 2014/11/28
Title of proceedings ACML 2014: Proceedings of the Sixth Asian Conference on Machine Learning
Editor(s) Phung,D
Li,H
Publication date 2014
Series JMLR Workshop and Conference Proceedings
Conference series Asian Conference on Machine Learning
Start page 283
End page 298
Total pages 6
Publisher JMLR Workshop and Conference Proceedings
Place of publication [Nha Trang, Vietnam]
Keyword(s) ordinal random fields
ordinal matrix factorisation
Markov random fields
collaborative filtering
Summary Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.
ISSN 1533-7928
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, The Author
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071557

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.