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• W2524138002 abstract "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. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as $d$-dimensional numerical vectors, as well as the price offered. The parameters of the choice model are a priori unknown to the firm, but can be learned as the (binary-valued) sales data accrues over time. The firm's objective is to minimize the regret, i.e., the expected revenue loss against a clairvoyant policy that knows the parameters of the choice model in advance, and always offers the revenue-maximizing price. This setting is motivated in part by the prevalence of online marketplaces that allow for real-time pricing. We assume a structured choice model, parameters of which depend on $s_0$ out of the $d$ product features. We propose a dynamic policy, called Regularized Maximum Likelihood Pricing (RMLP) that leverages the (sparsity) structure of the high-dimensional model and obtains a logarithmic regret in $T$. More specifically, the regret of our algorithm is of $O(s_0 log d cdot log T)$. Furthermore, we show that no policy can obtain regret better than $O(s_0 (log d + log T))$." @default.
• W2524138002 created "2016-10-07" @default.
• W2524138002 creator A5010342958 @default.
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• W2524138002 date "2016-09-24" @default.
• W2524138002 modified "2023-09-27" @default.
• W2524138002 title "Dynamic Pricing in High-dimensions" @default.
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• W2524138002 hasPublicationYear "2016" @default.
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