A Statistical Framework for Learning Preferences from the Past

Tamojit Sadhukhan; Moulinath Banerjee; Krishanu Maulik; Parthanil Roy

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arXiv · 2605.10042

A Statistical Framework for Learning Preferences from the Past

Tamojit Sadhukhan·Moulinath Banerjee·Krishanu Maulik·Parthanil Roy

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Abstract

In many real-world settings such as online recommendation or consumer choice modeling, individuals make repeated choices from a fixed set of options. Accurately estimating their underlying preferences is essential for generating personalized future recommendations. Probabilistic models for understanding user choice behavior from past decisions can serve as a valuable addition to existing recommender systems and choice prediction methods. To this end, in this article, we introduce a novel statistical framework for predicting user preferences based on their past choices, under a natural monotonicity assumption: options that were chosen more frequently or more intensely in the past are more likely to be chosen again in the future. Our approach builds on a parametric model proposed by Le Goff and Soulier (2017), originally used to describe how ants in an ant colony select a path among many pre-existing paths. We propose a non-parametric generalization of this model, drawing inspiration from the generalized elephant random walk introduced by Maulik et al. (2024). We develop a method of maximum likelihood estimation of the user preference probabilities under the above-mentioned monotonicity constraint. We also derive theoretical guarantees for our estimator and demonstrate the effectiveness of our method through both simulated experiments and real-world datasets.

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Tamojit Sadhukhan, Moulinath Banerjee, Krishanu Maulik, Parthanil Roy. 2026-05-11. A Statistical Framework for Learning Preferences from the Past. https://arxiv.org/abs/2605.10042

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