Summary of Improved Algorithms For Contextual Dynamic Pricing, by Matilde Tullii et al.
Improved Algorithms for Contextual Dynamic Pricing
by Matilde Tullii, Solenne Gaucher, Nadav Merlis, Vianney Perchet
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Data Structures and Algorithms (cs.DS); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a pricing strategy for sellers in contextual dynamic pricing settings. The goal is to maximize revenue by setting prices that are below buyers’ valuations, which depend on contextual information. Two valuation models are considered: one with linear dependencies and noise, and another without the linearity assumption. For both models, the authors develop algorithms that achieve optimal regret bounds. The first algorithm achieves a regret bound of (T^{2/3}), improving existing results, while the second algorithm obtains a regret of (T^{d+2/d+3}). These findings have implications for revenue maximization in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to set prices based on contextual information. Imagine you’re buying a concert ticket, and the price depends on who’s performing. The goal is to make as much money as possible by setting the right price. Two different ways of thinking about how buyers value products are studied. One assumes that the value depends linearly on the context and gets distorted by random noise. For this model, the authors develop a pricing strategy that does very well in the long run. The other model removes this linearity assumption and shows that the strategy still works well. |
