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[2602.16476] Learning Preference from Observed Rankings
Summary
This paper presents a framework for learning individual preferences from partial ranking data, enhancing recommendation systems by addressing exposure bias and improving prediction accuracy.
Why It Matters
Understanding consumer preferences is crucial for effective marketing and product recommendations. This research offers a novel approach to estimate preferences from observed rankings, which can lead to better-targeted recommendations and improved user satisfaction in various applications.
Key Takeaways
- Introduces a framework for learning preferences from partial rankings.
- Addresses exposure bias in observed comparisons to improve accuracy.
- Utilizes a stochastic gradient descent algorithm for efficient computation.
- Demonstrates improved recommendation performance using real transaction data.
- Enhances interpretability of preference models through product attributes.
Statistics > Machine Learning arXiv:2602.16476 (stat) [Submitted on 18 Feb 2026] Title:Learning Preference from Observed Rankings Authors:Yu-Chang Chen, Chen Chian Fuh, Shang En Tsai View a PDF of the paper titled Learning Preference from Observed Rankings, by Yu-Chang Chen and 2 other authors View PDF HTML (experimental) Abstract:Estimating consumer preferences is central to many problems in economics and marketing. This paper develops a flexible framework for learning individual preferences from partial ranking information by interpreting observed rankings as collections of pairwise comparisons with logistic choice probabilities. We model latent utility as the sum of interpretable product attributes, item fixed effects, and a low-rank user-item factor structure, enabling both interpretability and information sharing across consumers and items. We further correct for selection in which comparisons are observed: a comparison is recorded only if both items enter the consumer's consideration set, inducing exposure bias toward frequently encountered items. We model pair observability as the product of item-level observability propensities and estimate these propensities with a logistic model for the marginal probability that an item is observable. Preference parameters are then estimated by maximizing an inverse-probability-weighted (IPW), ridge-regularized log-likelihood that reweights observed comparisons toward a target comparison population. To scale computation, we propo...
