Properties of the Mallows Model Depending on the Number of Alternatives: AWarning for an Experimentalist

被引：0

作者：

Boehmer, Niclas ^{[1
]}

Faliszewski, Piotr ^{[2
]}

Kraiczy, Sonja ^{[3
]}

机构：

[1] TU Berlin, Berlin, Germany

[2] AGH Univ Sci & Technol, Krakow, Poland

[3] Univ Oxford, Oxford, England

来源：

INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 202 | 2023年 / 202卷

基金：

欧洲研究理事会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Mallows model is a popular distribution for ranked data. We empirically and theoretically analyze how the properties of rankings sampled from the Mallows model change when increasing the number of alternatives. We find that realworld data behaves differently from the Mallows model, yet is in line with its recent variant proposed by Boehmer et al. (2021). As part of our study, we issue several warnings about using the classic Mallows model. For instance, we find that one should be extremely careful when using the Mallows model to generate data for experiments with a varying number of alternatives, as observed trends in such experiments might be due to the changing nature of the generated data.

引用

页数：23

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