A grouped ranking model for item preference parameter

Hideitsu Hino, Yu Fujimoto, Noboru Murata

    Research output: Contribution to journalArticle

    3 Citations (Scopus)

    Abstract

    Given a set of rating data for a set of items, determining preference levels of items is amatter of importance. Various probability models have been proposed to solve this task. One such model is the Plackett-Luce model, which parameterizes the preference level of each item by a real value. In this letter, the Plackett-Luce model is generalized to cope with grouped ranking observations such as movie or restaurant ratings. Since it is difficult to maximize the likelihood of the proposed model directly, a feasible approximation is derived, and the em algorithm is adopted to find the model parameter by maximizing the approximate likelihood which is easily evaluated. The proposed model is extended to a mixture model, and two applications are proposed. To show the effectiveness of the proposed model, numerical experiments with real-world data are carried out.

    Original languageEnglish
    Pages (from-to)2417-2451
    Number of pages35
    JournalNeural Computation
    Volume22
    Issue number9
    DOIs
    Publication statusPublished - 2010 Sep

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    Restaurants
    Motion Pictures
    Datasets
    Ranking

    ASJC Scopus subject areas

    • Cognitive Neuroscience

    Cite this

    A grouped ranking model for item preference parameter. / Hino, Hideitsu; Fujimoto, Yu; Murata, Noboru.

    In: Neural Computation, Vol. 22, No. 9, 09.2010, p. 2417-2451.

    Research output: Contribution to journalArticle

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