arXiv 26 Dec 2023 · Machine Learning
arXiv:2312.15999 · PDF · DOI · OpenAlex · Extracted main text
We study an online contextual dynamic pricing problem, where customers decide whether to purchase a product based on its features and price. We introduce a novel approach to modeling a customer's expected demand by incorporating feature-based price elasticity, which can be equivalently represented as a valuation with heteroscedastic noise. To solve the problem, we propose a computationally efficient algorithm called "Pricing with Perturbation (PwP)", which enjoys an $O(\sqrt{dT\log T})$ regret while allowing arbitrary adversarial input context sequences. We also prove a matching lower bound at $\Omega(\sqrt{dT})$ to show the optimality regarding $d$ and $T$ (up to $\log T$ factors). Our results shed light on the relationship between contextual elasticity and heteroscedastic valuation, providing insights for effective and practical pricing strategies.
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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 | Xu, J. and Wang, Y.-X (2021) Logarithmic regret in feature-based dynamic pricing self | 0.971 | 12 | 5 | 92% |
| 2 | Javanmard, A. and Nazerzadeh, H (2019) Dynamic pricing in high-dimensions | 0.928 | 15 | 6 | 80% |
| 3 | Ban, G.-Y. and Keskin, N. B (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity | 0.874 | 8 | 2 | 100% |
| 4 | Wang, H., Talluri, K., and Li, X (2021) On dynamic pricing with covariates | 0.874 | 5 | 2 | 100% |
| 5 | Cohen, M. C., Lobel, I., and Paes Leme, R (2020) Feature-based dynamic pricing | 0.843 | 4 | 3 | 75% |
| 6 | Bu, J., Simchi-Levi, D., and Wang, C (2022) Context-based dynamic pricing with partially linear demand model | 0.737 | 3 | 2 | 100% |
| 7 | Miao, S., Chen, X., Chao, X., Liu, J., and Zhang, Y (2019) Context-based dynamic pricing with online clustering | 0.737 | 3 | 2 | 100% |
| 8 | Qiang, S. and Bayati, M (2016) Dynamic pricing with demand covariates | 0.737 | 3 | 2 | 100% |
| 9 | Broder, J. and Rusmevichientong, P (2012) Dynamic pricing under a general parametric choice model | 0.644 | 2 | 2 | 100% |
| 10 | Shah, V., Johari, R., and Blanchet, J (2019) Semi-parametric dynamic contextual pricing | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 42 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand | 0.000 | 1 | 1 |