Ningyuan Chen, Guillermo Gallego
arXiv 20 Dec 2018 · Machine Learning · publishedMathematics of Operations Research (2022) · 34 citations (OpenAlex)
arXiv:1812.09234 · PDF · DOI · OpenAlex · Extracted main text
We consider the problem of a firm seeking to use personalized pricing to sell an exogenously given stock of a product over a finite selling horizon to different consumer types. We assume that the type of an arriving consumer can be observed but the demand function associated with each type is initially unknown. The firm sets personalized prices dynamically for each type and attempts to maximize the revenue over the season. We provide a learning algorithm that is near-optimal when the demand and capacity scale in proportion. The algorithm utilizes the primal-dual formulation of the problem and learns the dual optimal solution explicitly. It allows the algorithm to overcome the curse of dimensionality (the rate of regret is independent of the number of types) and sheds light on novel algorithmic designs for learning problems with resource constraints.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Besbes, O. and A. Zeevi (2009) Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms | 1.000 | 7 | 5 | 100% |
| 2 | Wang, Z., S. Deng, and Y. Ye (2014) Close the gaps: A learning-while-doing algorithm for single-product revenue management problems | 0.971 | 12 | 5 | 92% |
| 3 | Slivkins, A (2014) Contextual bandits with similarity information | 0.874 | 8 | 2 | 100% |
| 4 | Besbes, O. and A. Zeevi (2012) Blind network revenue management | 0.874 | 7 | 2 | 100% |
| 5 | Chen, Q., S. Jasin, and I. Duenyas (2019) Nonparametric self-adjusting control for joint learning and optimization of multiproduct pricing with finite resource capacity | 0.811 | 4 | 2 | 100% |
| 6 | Badanidiyuru, A., R. Kleinberg, and A. Slivkins (2013) Bandits with knapsacks | 0.737 | 3 | 2 | 100% |
| 7 | Gallego, G. and G. Van Ryzin (1997) A multiproduct dynamic pricing problem and its applications to network yield management self | 0.737 | 3 | 2 | 100% |
| 8 | Agrawal, S. and N. R. Devanur (2014) Bandits with concave rewards and convex knapsacks | 0.644 | 2 | 2 | 100% |
| 9 | Chen, N. and G. Gallego (2019) Nonparametric pricing analytics with customer covariates self | 0.644 | 2 | 2 | 100% |
| 10 | Chen, Y. and C. Shi (2019) Network revenue management with online inverse batch gradient descent method | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning | 0.405 | 1 | 1 |