title: A grouped ranking model for item preference parameter.
abstract: Given a set of rating data for a set of items, determining preference levels of items is a matter 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.
authors.affiliation: School of Science and Engineering, Waseda University, Shinjuku, Tokyo 169-8555, Japan. hideitsu.hino@toki.waseda.jp
authors.forename: Hideitsu
authors.initials: H
authors.lastname: Hino
authors.forename: Yu
authors.initials: Y
authors.lastname: Fujimoto
authors.forename: Noboru
authors.initials: N
authors.lastname: Murata
mesh_terms: D000465:Algorithms; D015233:Models, Statistical; D011336:Probability