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- W2288578912 abstract "We consider the problem faced by a firm that receives highly differentiated products in an online fashion and needs to set prices so as to sell them to its customer base. Products are described by vectors of features and the market value of each product is assumed to be linear in the values of the features. The firm does not initially know the values of the different features, but it can learn the values of the features based on whether products sold at the posted prices in the past. This model is motivated by a question in online advertising where impressions arrive over time and can be described by vectors of features. We first consider a multi-dimensional version of binary search over polyhedral sets, and show that it has exponential worst-case regret in the dimension of the feature space. We then show that a modification of the prior algorithm, where uncertainty sets are replaced by their Lowner-John ellipsoids has a worst-case regret that is quadratic in the dimension of the feature space and logarithmic in the time horizon." @default.
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- W2288578912 date "2016-01-01" @default.
- W2288578912 modified "2023-10-17" @default.
- W2288578912 title "Feature-Based Dynamic Pricing" @default.
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- W2288578912 doi "https://doi.org/10.2139/ssrn.2737045" @default.
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