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プレケット・ルースモデル×ランク集約法×
分野意思決定意思決定
系統Regression modelMachine learning
提唱年19752001
提唱者Robin Plackett; R. Duncan LuceDwork, Kumar, Naor & Sivakumar
種類Probabilistic ranking modelCombinatorial ranking method
原典Plackett, R. L. (1975). The analysis of permutations. Journal of the Royal Statistical Society: Series C, 24(2), 193–202. DOI ↗Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001). Rank aggregation methods for the web. Proceedings of the 10th International Conference on World Wide Web, 613–622. DOI ↗
別名Luce's Choice Axiom Model, Rank-Ordered Logit Model, Exploded Logit Model, Sıralama Tercih ModeliRank Fusion, Order Aggregation, Preference Aggregation, Sıralama Birleştirme
関連32
概要The Plackett-Luce model is a probabilistic framework for analysing and predicting rank-ordered data. Introduced by Robin Plackett (1975) — building on R. Duncan Luce's earlier axiom of choice (1959) — it models the probability of any complete ranking of items as a sequential selection process, where each item's chance of being chosen at each position is proportional to its latent worth parameter. It is widely used in preference learning, recommender systems, and choice modelling.Rank Aggregation is a family of methods that combine multiple ranked lists of alternatives into a single consensus ranking. Formally studied in the context of web search by Dwork, Kumar, Naor, and Sivakumar (2001), these methods address the problem of synthesizing divergent preference orderings from multiple sources — such as search engines, expert judges, or voter ballots — into one coherent, representative ordering that minimizes overall disagreement across the input rankings.
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ScholarGate手法を比較: Plackett-Luce Model · Rank Aggregation. 2026-06-18に以下より取得 https://scholargate.app/ja/compare