方法对比

	Bradley-Terry 模型	多项逻辑回归	排序聚合方法
领域	决策	计量经济学	决策
方法族	Regression model	Regression model	Machine learning
起源年份	1952	1974	2001
提出者	Ralph Bradley & Milton Terry	McFadden	Dwork, Kumar, Naor & Sivakumar
类型	Probabilistic paired comparison model	Multinomial logistic regression	Combinatorial ranking method
开创性文献	Bradley, R. A., & Terry, M. E. (1952). Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika, 39(3/4), 324–345.	McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503	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.
别名	BT Model, Bradley-Terry-Luce Model, Paired Comparison Model, İkili Karşılaştırma Modeli	multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon	Rank Fusion, Order Aggregation, Preference Aggregation, Sıralama Birleştirme
相关	3	5	2
摘要	The Bradley-Terry model is a probabilistic model for paired comparisons that assigns a latent strength parameter to each item and predicts the probability that one item beats another in a head-to-head contest. Introduced by Ralph A. Bradley and Milton E. Terry in 1952, it provides a principled statistical framework for ranking items from pairwise preference data, including incomplete comparison designs where not every pair is directly observed.	Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category.	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.