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An Online Algorithm for Learning Buyer Behavior under Realistic Pricing Restrictions

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  • Debjyoti Saharoy
  • Theja Tulabandhula

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.

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  • Debjyoti Saharoy & Theja Tulabandhula, 2018. "An Online Algorithm for Learning Buyer Behavior under Realistic Pricing Restrictions," Papers 1803.01968, arXiv.org.
    • Handle: RePEc:arx:papers:1803.01968
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    References listed on IDEAS

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    1. Omar Besbes & Assaf Zeevi, 2015. "On the (Surprising) Sufficiency of Linear Models for Dynamic Pricing with Demand Learning," Management Science, INFORMS, vol. 61(4), pages 723-739, April.
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