Virag Shah, Jose Blanchet, Ramesh Johari
arXiv 7 Jan 2019 · Machine Learning · 7 citations (OpenAlex)
arXiv:1901.02045 · PDF · DOI · OpenAlex · Extracted main text
Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product. However, her true valuation is unobservable to the seller, only binary outcome in the form of success-failure of a transaction is observed. Unlike in usual contextual bandit settings, the optimal price/arm given a covariate in our setting is sensitive to the detailed characteristics of the residual uncertainty distribution. We develop a semi-parametric model in which the residual distribution is non-parametric and provide the first algorithm which learns both regression parameters and residual distribution with $\tilde O(\sqrt{n})$ regret. We empirically test a scalable implementation of our algorithm and observe good performance.
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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 | Kleinberg, R. and Leighton, T (2003) The value of knowing a demand curve: Bounds on regret for online posted-price auctions | 1.000 | 9 | 3 | 100% |
| 2 | Plan, Y. and Vershynin, R (2013) Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach | 0.874 | 5 | 2 | 100% |
| 3 | Cohen, M. C., Lobel, I., and Paes Leme, R (2016) Feature-based dynamic pricing | 0.644 | 4 | 1 | 100% |
| 4 | Ban, G.-Y. and Keskin, N. B (2019) Personalized dynamic pricing with machine learning | 0.585 | 3 | 1 | 100% |
| 5 | Javanmard, A. and Nazerzadeh, H (2019) Dynamic pricing in high-dimensions | 0.585 | 3 | 1 | 100% |
| 6 | Mao, J., Leme, R., and Schneider, J (2018) Contextual pricing for lipschitz buyers | 0.585 | 3 | 1 | 100% |
| 7 | Nambiar, M., Simchi-Levi, D., and Wang, H (2019) Dynamic learning and pricing with model misspecification | 0.585 | 3 | 1 | 100% |
| 8 | Qiang, S. and Bayati, M (2019) Dynamic pricing with demand covariates | 0.585 | 3 | 1 | 100% |
| 9 | Alon, N., Cesa-Bianchi, N., Gentile, C., and Mansour, Y (2013) From bandits to experts: A tale of domination and independence | 0.405 | 1 | 1 | 100% |
| 10 | Amin, K., Rostamizadeh, A., and Syed, U (2014) Repeated contextual auctions with strategic buyers | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 27 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Policy Optimization Using Semi-parametric Models for Dynamic Pricing | 0.811 | 4 | 2 |
| 2 | Pricing with Contextual Elasticity and Heteroscedastic Valuation | 0.644 | 2 | 2 |
| 3 | Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand | 0.000 | 2 | 1 |