Papers › Improved Algorithms for Contextual Dynamic Pricing
Improved Algorithms for Contextual Dynamic Pricing
Matilde Tullii, Solenne Gaucher, Nadav Merlis, Vianney Perchet
In contextual dynamic pricing, a seller sequentially prices goods based on contextual information. Buyers will purchase products only if the prices are below their valuations. The goal of the seller is to design a pricing strategy that collects as much revenue as possible. We focus on two different valuation models. The first assumes that valuations linearly depend on the context and are further distorted by noise. Under minor regularity assumptions, our algorithm achieves an optimal regret bound of 𝒪̃(T^(2/3)), improving the existing results. The second model removes the linearity assumption, requiring only that the expected buyer valuation is β-H\"older in the context. For this model, our algorithm obtains a regret 𝒪̃(T^(d+2β/d+3β)), where d is the dimension of the context space.
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