马洛斯模型的特性取决于备选方案的数量：给实验者的警告

International Conference on Machine Learning Pub Date : 2024-01-25 DOI:10.48550/arXiv.2401.14562

Niclas Boehmer, Piotr Faliszewski, Sonja Kraiczy

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引用次数: 1

摘要

Mallows 模型是排名数据的一种流行分布。我们从经验和理论上分析了当备选方案数量增加时，从 Mallows 模型中采样的排名属性会发生怎样的变化。我们发现，现实世界的数据表现与 Mallows 模型不同，但却与 Boehmer 等人最近提出的变体一致[2021]。作为研究的一部分，我们对该模型的使用提出了一些警告。

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

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 real-world data behaves differently than 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 model.

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International Conference on Machine Learning

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