A grouped ranking model for item preference parameter

Hideitsu Hino*, Yu Fujimoto, Noboru Murata

*Corresponding author for this work

Research output: Contribution to journalLetterpeer-review

4 Citations (Scopus)


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
Issue number9
Publication statusPublished - 2010 Sept

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience


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