A Hyper-surface Arrangement Model of Ranking Distributions

Shizuo Kaji, Akira Horiguchi, Takuro Abe, Yohsuke Watanabe

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

A distribution on the permutations over a fixed finite set is called a ranking distribution. Modelling ranking distributions is one of the major topics in preference learning as such distributions appear as the ranking data produced by many judges. In this paper, we propose a geometric model for ranking distributions. Our idea is to use hyper-surface arrangements in a metric space as the representation space, where each component cut out by hyper-surfaces corresponds to a total ordering, and its volume is proportional to the probability. In this setting, the union of components corresponds to a partial ordering and its probability is also estimated by the volume. Similarly, the probability of a partial ordering conditioned by another partial ordering is estimated by the ratio of volumes. We provide a simple iterative algorithm to fit our model to a given dataset. We show our model can represent the distribution of a real-world dataset faithfully and can be used for prediction and visualisation purposes.

本文言語英語
ホスト出版物のタイトルKDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
出版社Association for Computing Machinery
ページ796-804
ページ数9
ISBN(電子版)9781450383325
DOI
出版ステータス出版済み - 8 14 2021
イベント27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, シンガポール
継続期間: 8 14 20218 18 2021

出版物シリーズ

名前Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

会議

会議27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
国/地域シンガポール
CityVirtual, Online
Period8/14/218/18/21

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 情報システム

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