Item preference parameters from grouped ranking observations

Hideitsu Hino; Yu Fujimoto; Noboru Murata

doi:10.1007/978-3-642-01307-2_91

Item preference parameters from grouped ranking observations

Hideitsu Hino^*, Yu Fujimoto, Noboru Murata

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

Given a set of rating data for a set of items, determining the values of items is a matter of importance. Various probability models have been proposed to solve this task. The Plackett-Luce model is one of such models, which parametrizes the value of each item by a real valued preference parameter. In this paper, the Plackett-Luce model is generalized to cope with the grouped ranking observations such as movies or restaurants ratings. Since the maximization of the likelihood of the proposed model is computationally intractable, the lower bound of the likelihood which is easy to evaluate is derived, and the em algorithm is adopted to the maximization of the lower bound.

Original language	English
Title of host publication	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages	875-882
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-01307-2_91
Publication status	Published - 2009
Event	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand Duration: 2009 Apr 27 → 2009 Apr 30

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5476 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Country/Territory	Thailand
City	Bangkok
Period	09/4/27 → 09/4/30

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-642-01307-2_91

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Hino, H., Fujimoto, Y., & Murata, N. (2009). Item preference parameters from grouped ranking observations. In 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 (pp. 875-882). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI). https://doi.org/10.1007/978-3-642-01307-2_91

Item preference parameters from grouped ranking observations. / Hino, Hideitsu; Fujimoto, Yu ; Murata, Noboru.
13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. 2009. p. 875-882 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5476 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hino, H, Fujimoto, Y & Murata, N 2009, Item preference parameters from grouped ranking observations. in 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5476 LNAI, pp. 875-882, 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, Bangkok, Thailand, 09/4/27. https://doi.org/10.1007/978-3-642-01307-2_91

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Item preference parameters from grouped ranking observations

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