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. Attached Files Name Description MIMEType Size Downloads tran-learningfromordered-2012.pdf Published version application/pdf 3.19MB 23 Title Learning from ordered sets and applications in collaborative ranking Author(s) Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Dinh orcid.org/0000-0002-9977-8247 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30052642 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment Centre for Pattern Recognition and Data Analytics (PRADA) Open Access Collection 2018 ERA Submission Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 24 May 2013, 12:09:49 EST Fri, 24 May 2013, 13:47:51 EST Fri, 24 May 2013, 14:20:00 EST Mon, 27 May 2013, 08:45:49 EST Mon, 27 May 2013, 09:07:46 EST Mon, 27 May 2013, 09:11:37 EST Mon, 10 Jun 2013, 10:30:31 EST Wed, 21 Oct 2015, 14:47:02 EST Thu, 03 Mar 2016, 13:23:32 EST Tue, 06 Sep 2016, 21:05:26 EST Tue, 05 Dec 2017, 12:40:44 EST Fri, 07 Sep 2018, 16:30:27 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 736 Abstract Views, 23 File Downloads  -  Detailed Statistics Created: Fri, 24 May 2013, 12:09:49 EST