Document Detail

A grouped ranking model for item preference parameter.
MedLine Citation:
PMID:  20569175     Owner:  NLM     Status:  MEDLINE    
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.
Hideitsu Hino; Yu Fujimoto; Noboru Murata
Related Documents :
20064885 - Construct validity of a mammography processes of change scale and invariance by stage o...
19569195 - Ultra scale-down approach to correct dispersive and retentive effects in small-scale co...
10269425 - Method for the study of the effectiveness of attendance in the multiple-family group on...
22468465 - The development of lower limb musculoskeletal models with clinical relevance is depende...
17438795 - Application of land use regression to estimate long-term concentrations of traffic-rela...
21381025 - Development and psychometric evaluation of the milwaukee psychotherapy expectations que...
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    
School of Science and Engineering, Waseda University, Shinjuku, Tokyo 169-8555, Japan.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Models, Statistical*

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

Previous Document:  Bayesian online learning of the hazard rate in change-point problems.
Next Document:  Individual differences in nucleus accumbens dopamine receptors predict development of addiction-like...