A Grouped Ranking Model for Item Preference Parameter

作者:Hino Hideitsu*; Fujimoto Yu; Murata Noboru
来源:Neural Computation, 2010, 22(9): 2417-2451.
DOI:10.1162/NECO_a_00008

摘要

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.

  • 出版日期2010-9