Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data
This addresses the problem of setting effective prices in e-commerce or retail for sellers with limited transaction data, offering an incremental improvement by providing a tractable method without needing demand estimation.
The paper tackles the off-policy contextual pricing problem where only transaction data (offered prices and purchase decisions) is available, not customer valuations, by introducing convex surrogate loss functions that directly optimize pricing policies with expected revenue guarantees. It demonstrates through simulations that this approach is competitive with or outperforms state-of-the-art methods in some settings.
We study an off-policy contextual pricing problem where the seller has access to samples of prices that customers were previously offered, whether they purchased at that price, and auxiliary features describing the customer and/or item being sold. This is in contrast to the well-studied setting in which samples of the customer's valuation (willingness to pay) are observed. In our setting, the observed data is influenced by the previous pricing policy, and we do not know how customers would have responded to alternative prices. We introduce suitable loss functions for this setting that can be directly optimized to find an effective pricing policy with expected revenue guarantees, without the need for estimation of an intermediate demand function. We focus on convex loss functions. This is particularly relevant when linear pricing policies are desired for interpretability reasons, resulting in a tractable convex revenue optimization problem. We propose generalized hinge and quantile pricing loss functions that price at a multiplicative factor of the conditional expected valuation or a particular quantile of the prices that sold, despite the valuation data not being observed. We prove expected revenue bounds for these pricing policies respectively when the valuation distribution is log-concave, and we provide generalization bounds for the finite sample case. Finally, we conduct simulations on both synthetic and real-world data to demonstrate that this approach is competitive with, and in some settings outperforms, state-of-the-art methods in contextual pricing.