Item preference parameters from grouped ranking observations

Hideitsu Hino, Yu Fujimoto, Noboru Murata

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
ページ875-882
ページ数8
DOI
出版ステータスPublished - 2009 7 23
イベント13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand
継続期間: 2009 4 272009 4 30

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5476 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
CountryThailand
CityBangkok
Period09/4/2709/4/30

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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