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    A sequential decision approach to ordinal preferences in recommender systems

    Access Status
    Fulltext not available
    Authors
    Tran, Truyen
    Phung, D.
    Venkatesh, S.
    Date
    2012
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Tran, T. and Phung, D. and Venkatesh, S. 2012. A sequential decision approach to ordinal preferences in recommender systems, in Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAA-12), Jul 22-26 2012, pp. 676-682. Toronto, Canada: AAAI Press.
    Source Title
    Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligance
    Source Conference
    AAAI Conference on Artificial Intelligence
    Additional URLs
    https://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/viewFile/4975/5256
    ISBN
    978-1-57735-568-7
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/40131
    Collection
    • Curtin Research Publications
    Abstract

    We propose a novel sequential decision approach to modeling ordinal ratings in collaborative filtering problems. The rating process is assumed to start from the lowest level, evaluates against the latent utility at the corresponding level and moves up until a suitable ordinal level is found. Crucial to this generative process is the underlying utility random variables that govern the generation of ratings and their modelling choices. To this end, we make a novel use of the generalised extreme value distributions, which is found to be particularly suitable for our modeling tasks and at the same time, facilitate our inference and learning procedure. The proposed approach is flexible to incorporate features from both the user and the item. We evaluate the proposed framework on three well-known datasets: MovieLens, Dating Agency and Netflix. In all cases, it is demonstrated that the proposed work is competitive against state-of-the-art collaborative filtering methods.

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