Dynamic Pricing in High-dimensions
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We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. This is motivated in part by the prevalence of online marketplaces that allow for real-time pricing. We propose a dynamic policy, called Regularized Maximum Likelihood Pricing (RMLP), that obtains asymptotically optimal revenue. Our policy leverages the structure (sparsity) of a high-dimensional demand space in order to obtain a logarithmic regret compared to the clairvoyant policy that knows the parameters of the demand in advance. More specifically, the regret of our algorithm is of O(s_0 T ( d + T)), where d and s_0 correspond to the dimension of the demand space and its sparsity. Furthermore, we show that no policy can obtain regret better than O(s_0 ( d + T)).
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