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    Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering

    173057_50458_Truyen_etal_sdm11.pdf (161.7Kb)
    Access Status
    Open access
    Authors
    Tran, Truyen
    Phung, Dinh
    Venkatesh, Svetha
    Date
    2011
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Tran, Truyen The and Phung, Dinh Q. and Venkatesh, Svetha. 2011. Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering, in Proceedings of 2011 Siam International conference on Data Mining (SDM), Apr 28-30 2011. Mesa, Arizona: Omnipress
    Source Title
    2011 SIAM Int. Conference on Data Mining
    Source Conference
    SDM 2011
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/17402
    Collection
    • Curtin Research Publications
    Abstract

    Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, one often resorts to document rating, in that a subset of documents is assigned with a small number indicating the degree of relevance. This poses a general problem of modelling and learning rank data with ties. In this paper, we propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stage wise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on two application areas: (i) document ranking with the data from the recently held Yahoo! challenge, and (ii) collaborative filtering with movie data. The results demonstrate that the models are competitive against well-known rivals.

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