An Online Algorithm for Learning Buyer Behavior under Realistic Pricing Restrictions

TL;DR

This paper introduces an efficient online algorithm that learns buyer behavior under realistic pricing constraints, capable of handling non-linear utilities, addressing practical limitations of previous methods.

Contribution

The paper presents a novel online learning algorithm that accommodates non-linear buyer utilities and arbitrary price restrictions, improving practical applicability.

Findings

Successfully learns buyer parameters in real-time.

Handles non-linear utility functions effectively.

Operates under arbitrary pricing restrictions.

Abstract

We propose a new efficient online algorithm to learn the parameters governing the purchasing behavior of a utility maximizing buyer, who responds to prices, in a repeated interaction setting. The key feature of our algorithm is that it can learn even non-linear buyer utility while working with arbitrary price constraints that the seller may impose. This overcomes a major shortcoming of previous approaches, which use unrealistic prices to learn these parameters making them unsuitable in practice.