提出改进的排名偏好模型，更准确预测用户只关注少数优选项的行为。
Generalized Top-k Mallows Model for Ranked Choices
- 设计了专用于广义top-k Mallows模型的新采样方法。
- 实现了高效计算选择概率，支持大规模应用。
- 提供主动学习算法，从实际选择数据中快速估计模型参数。
经典Mallows模型是建模用户偏好的基础工具，但在现实场景中存在局限：用户通常只关注有限数量的优选项目，对其他项目无差别。为解决此问题，已有研究提出top-k Mallows模型以更好地匹配实际应用。本文针对广义top-k Mallows模型面临的关键挑战，提出三项核心贡献：(1) 一种专为该模型设计的新型采样方案；(2) 高效计算选择概率的算法；(3) 从观测选择数据中估计模型参数的主动学习算法。这些成果为关键决策场景中的分析与预测提供了新工具。我们进行了严谨的数学性能分析，并通过合成数据与真实数据的广泛实验，验证了所提方法在可扩展性与准确性上的优势。同时，对比了Mallows模型在预测top-k列表时相较于更简单的多项对数回归模型的更强表现。
原文摘要 · Abstract (English)
The classic Mallows model is a foundational tool for modeling user preferences. However, it has limitations in capturing real-world scenarios, where users often focus only on a limited set of preferred items and are indifferent to the rest. To address this, extensions such as the top-k Mallows model have been proposed, aligning better with practical applications. In this paper, we address several challenges related to the generalized top-k Mallows model, with a focus on analyzing buyer choices. Our key contributions are: (1) a novel sampling scheme tailored to generalized top-k Mallows models, (2) an efficient algorithm for computing choice probabilities under this model, and (3) an active learning algorithm for estimating the model parameters from observed choice data. These contributions provide new tools for analysis and prediction in critical decision-making scenarios. We present a rigorous mathematical analysis for the performance of our algorithms. Furthermore, through extensive experiments on synthetic data and real-world data, we demonstrate the scalability and accuracy of our proposed methods, and we compare the predictive power of Mallows model for top-k lists compared to the simpler Multinomial Logit model.
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