A Statistical Framework for Learning Preferences from the Past

TL;DR

This paper introduces a non-parametric statistical framework for learning user preferences from past choices, leveraging a monotonicity assumption and maximum likelihood estimation, with proven guarantees and empirical validation.

Contribution

It generalizes a parametric model to a non-parametric setting and provides a new method for preference estimation with theoretical and experimental support.

Findings

The proposed estimator has provable theoretical guarantees.

The method performs well on both simulated and real-world data.

It effectively captures user preferences under monotonicity assumptions.

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.