An Online Algorithm for Learning Buyer Behavior under Realistic Pricing Restrictions
This work addresses a practical limitation in pricing and revenue optimization for sellers, though it appears incremental as it builds on existing learning frameworks with a focus on realistic constraints.
The paper tackles the problem of learning buyer behavior under realistic pricing constraints in repeated seller-buyer interactions, proposing an efficient online algorithm that can handle non-linear utility and arbitrary price restrictions, overcoming the impracticality of prior methods that relied on unrealistic prices.
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.