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    Learning From Ordered Sets and Applications in Collaborative Ranking

    Access Status
    Fulltext not available
    Authors
    Tran, Truyen
    Phung, D.
    Venkatesh, S.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    Tran, T. and Phung, D. and Venkatesh, S. 2012. Learning From Ordered Sets and Applications in Collaborative Ranking, in Hoi, S.C.H. & Buntine, W. (ed), Fourth Asian Conference on Machine Learning (ACML 2012), Nov 4-6 2012, pp. 427-442. Singapore: Journal of Machine Learning Research (JMLR).
    Source Title
    JMLR Workshop and Conference Proceedings, Volume 25: Asian Conference on Machine Learning
    Source Conference
    Fourth Asian Conference on Machine Learning (ACML 2012)
    Additional URLs
    http://www.jmlr.org/proceedings/papers/v25/
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/8284
    Collection
    • Curtin Research Publications
    Abstract

    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.

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