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