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Learning from ordered sets and applications in collaborative ranking

Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2012, Learning from ordered sets and applications in collaborative ranking, in ACML 2012 : Proceedings of the 4th Asian Conference on Machine Learning, JMLR : workshop and conference proceedings, [Singapore], pp. 427-442.

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Title Learning from ordered sets and applications in collaborative ranking
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Asian Conference on Machine Learning (4th : 2012 : Singapore)
Conference location Singapore
Conference dates 4-6 Nov. 2012
Title of proceedings ACML 2012 : Proceedings of the 4th Asian Conference on Machine Learning
Editor(s) Hoi, Steven C.H.
Buntine, Wray
Publication date 2012
Conference series Asian Conference on Machine Learning
Start page 427
End page 442
Total pages 16
Publisher JMLR : workshop and conference proceedings
Place of publication [Singapore]
Keyword(s) ordered sets
ranking with ties
split-merge
MCMC
latent models
Boltzmann machines
collaborative filtering
Summary Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!/2)6.93145N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the state-space efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080110 Simulation and Modelling
Socio Economic Objective 899999 Information and Communication Services not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30052642

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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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.