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Abstract:
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Dynamic pricing becomes a common practice nowadays in e-commerce such as hospitality and tourism industry, air-transportation or ride-share service. However, dynamic pricing algorithm is known to face the so-called cold start issue that no valid inferences can be drawn for users or items before sufficient information is fetched. In addition, pricing for product with high-dimensional features typically requires a fine tuning scheme on regularization to ensure good sale performance, enhance interpretation or even manage risk. In this talk, we study high dimensional dynamic pricing algorithm based on online Lasso procedure, where the customer transaction behavior is described by a structured choice model. We device a theoretical tool, termed time-uniform risk envelope, to manage the risk over the whole time horizon including the cold start period. This risk envelope result suggests an online tuning scheme that is adapted to different specific tasks in market demand dynamic such as maximizing the revenue or identifying important product valuation feature. Regret of dynamic pricing algorithm with the online tuning scheme is developed and supported by simulations.
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