# Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization

arXiv: https://arxiv.org/abs/2405.14909 · HTML: https://mlsanity.com/paper/2405.14909/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 3 of 3)

- [Model Distillation for Revenue Optimization: Interpretable Personalized Pricing](https://mlsanity.com/paper/2007.01903/) (2020)
- [Optimization Beyond Prediction: Prescriptive Price Optimization](https://mlsanity.com/paper/1605.05422/) (2016)
- [A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Data](https://mlsanity.com/paper/1612.06589/) (2016)

## Built on by (most cited first, 1 of 1)

- [Robust personalized pricing under uncertainty of purchase probabilities](https://mlsanity.com/paper/2407.15332/) (2024)

## Abstract (arXiv)

This study addresses the interpretable estimation of price bounds in the context of price optimization. In recent years, price-optimization methods have become indispensable for maximizing revenue and profits. However, effective application of these methods to real-world pricing operations remains a significant challenge. It is crucial for operators responsible for setting prices to utilize reasonable price bounds that are not only interpretable but also acceptable. Despite this necessity, most studies assume that price bounds are given constant values, and few have explored reasonable determinations of these bounds. Therefore, we propose a comprehensive framework for determining price bounds that includes both the estimation and adjustment of these bounds. Specifically, we first estimate price bounds using three distinct approaches based on historical pricing data. Then, we adjust the estimated price bounds by solving an optimization problem that incorporates shape constraints. This method allows the implementation of price optimization under practical and reasonable price bounds suitable for real-world applications. We report the effectiveness of our proposed method through numerical experiments using historical pricing data from actual services.