arXiv 20 Feb 2021 · Machine Learning · 1 citations (OpenAlex)
arXiv:2102.10221 · PDF · DOI · OpenAlex · Extracted main text
Feature-based dynamic pricing is an increasingly popular model of setting prices for highly differentiated products with applications in digital marketing, online sales, real estate and so on. The problem was formally studied as an online learning problem [Javanmard & Nazerzadeh, 2019] where a seller needs to propose prices on the fly for a sequence of $T$ products based on their features $x$ while having a small regret relative to the best -- "omniscient" -- pricing strategy she could have come up with in hindsight. We revisit this problem and provide two algorithms (EMLP and ONSP) for stochastic and adversarial feature settings, respectively, and prove the optimal $O(d\log{T})$ regret bounds for both. In comparison, the best existing results are $O\left(\min\left{\frac{1}{\lambda_{\min}^2}\log{T}, \sqrt{T}\right}\right)$ and $O(T^{2/3})$ respectively, with $\lambda_{\min}$ being the smallest eigenvalue of $\mathbb{E}[xx^T]$ that could be arbitrarily close to $0$. We also prove an $\Omega(\sqrt{T})$ information-theoretic lower bound for a slightly more general setting, which demonstrates that "knowing-the-demand-curve" leads to an exponential improvement in feature-based dynamic pricing.
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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 | J. Broder and P. Rusmevichientong (2012) Dynamic pricing under a general parametric choice model | 1.000 | 5 | 3 | 100% |
| 2 | A. Krishnamurthy, T. Lykouris, C. Podimata, and R. Schapire (2021) Contextual search in the presence of irrational agents | 0.950 | 7 | 4 | 86% |
| 3 | A. Javanmard and H. Nazerzadeh (2019) Dynamic pricing in high-dimensions | 0.937 | 17 | 7 | 82% |
| 4 | M. C. Cohen, I. Lobel, and R. Paes Leme (2020) Feature-based dynamic pricing | 0.928 | 20 | 6 | 80% |
| 5 | R. Kleinberg and T. Leighton (2003) The value of knowing a demand curve: Bounds on regret for online posted-price auctions | 0.874 | 6 | 4 | 67% |
| 6 | P. Auer, N. Cesa-Bianchi, Y. Freund, and R. E. Schapire (2002) The nonstochastic multiarmed bandit problem | 0.737 | 4 | 3 | 50% |
| 7 | K. Amin, A. Rostamizadeh, and U. Syed (2014) Repeated contextual auctions with strategic buyers | 0.737 | 3 | 2 | 100% |
| 8 | A. Liu, R. P. Leme, and J. Schneider (2021) Optimal contextual pricing and extensions | 0.693 | 5 | 1 | 100% |
| 9 | R. P. Leme and J. Schneider (2018) Contextual search via intrinsic volumes | 0.585 | 3 | 1 | 100% |
| 10 | A. Agarwal, D. Hsu, S. Kale, J. Langford, L. Li, and R. Schapire (2014) Taming the monster: A fast and simple algorithm for contextual bandits | 0.511 | 3 | 2 | 33% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.