Learning from ordered sets and applications in collaborative ranking

Truyen Tran, Dinh Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


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 statespace 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.

Original languageEnglish
Title of host publication4th Asian Conference on Machine Learning, ACML 2012
Number of pages16
Publication statusPublished - 1 Dec 2012
Externally publishedYes
EventAsian Conference on Machine Learning 2012 - Singapore, Singapore
Duration: 4 Nov 20126 Nov 2012
Conference number: 4th
http://proceedings.mlr.press/v25/ (Proceedings)

Publication series

NameJournal of Machine Learning Research
PublisherJournal of Machine Learning Research (JMLR)
ISSN (Print)1532-4435


ConferenceAsian Conference on Machine Learning 2012
Abbreviated titleACML 2012
Internet address


  • Boltzmann machines
  • Collaborative filtering
  • Latent models
  • MCMC
  • Ordered sets
  • Ranking with ties
  • Split-merge

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