Poster Session 3 · Thursday, December 4, 2025 11:00 AM → 2:00 PM
#3310 Spotlight
Generalized Top-k Mallows Model for Ranked Choices
Abstract
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- Mallows model have been proposed, aligning better with practical applications.
In this paper, we address several challenges related to the generalized top- Mallows model, with a focus on analyzing buyer choices. Our key contributions are:
- a novel sampling scheme tailored to generalized top- Mallows models
- an efficient algorithm for computing choice probabilities under this model
- 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- lists compared to the simpler Multinomial Logit model.
