Document Detail


A grouped ranking model for item preference parameter.
MedLine Citation:
PMID:  20569175     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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:
Hideitsu Hino; Yu Fujimoto; Noboru Murata
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Neural computation     Volume:  22     ISSN:  1530-888X     ISO Abbreviation:  Neural Comput     Publication Date:  2010 Sep 
Date Detail:
Created Date:  2010-08-03     Completed Date:  2010-11-22     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9426182     Medline TA:  Neural Comput     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2417-51     Citation Subset:  IM    
Affiliation:
School of Science and Engineering, Waseda University, Shinjuku, Tokyo 169-8555, Japan. hideitsu.hino@toki.waseda.jp
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Models, Statistical*
Probability

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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