Context-Based Dynamic Pricing with Separable Demand Models

Published Online:https://doi.org/10.1287/mnsc.2022.02260

References

  • Abbasi-Yadkori Y, Pál D, Szepesvári C (2011) Improved algorithms for linear stochastic bandits. Shawe-Taylor J, Zemel R, Bartlett P, Pereira F, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems, vol. 24 (Curran Associates, Inc., Red Hook, NY), 2312–2320. Google Scholar
  • Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-time analysis of the multiarmed bandit problem. Machine Learn. 47(2–3):235–256.CrossrefGoogle Scholar
  • Ban GY, Keskin NB (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.LinkGoogle Scholar
  • Besbes O, Zeevi A (2009) Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res. 57(6):1407–1420.LinkGoogle Scholar
  • Besbes O, Zeevi A (2012) Blind network revenue management. Oper. Res. 60(6):1537–1550.LinkGoogle Scholar
  • Besbes O, Zeevi A (2015) On the (surprising) sufficiency of linear models for dynamic pricing with demand learning. Management Sci. 61(4):723–739.LinkGoogle Scholar
  • Bretagnolle J, Huber C (1979) Estimation des densités: Risque minimax. Zeitschrift Wahrscheinlichkeitstheorie Verwandte Gebiete 47(2):119–137.CrossrefGoogle Scholar
  • Broder J, Rusmevichientong P (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980.LinkGoogle Scholar
  • Cavanaugh JE, Neath AA (2019) The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdisciplinary Rev. Comput. Statist. 11(3):e1460.CrossrefGoogle Scholar
  • Chen N, Gallego G (2021) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.LinkGoogle Scholar
  • Chen N, Hu M (2023) Data-driven revenue management: The interplay of data, model, and decisions. Service Sci. 15(2):79–91.LinkGoogle Scholar
  • Chen Y, Shi C (2023) Network revenue management with online inverse batch gradient descent method. Production Oper. Management 32(7):2123–2137.Google Scholar
  • Chen X, Jasin S, Shi C (2022) The Elements of Joint Learning and Optimization in Operations Management, vol. 18 (Springer Nature, New York).CrossrefGoogle Scholar
  • Chu W, Li L, Reyzin L, Schapire R (2011) Contextual bandits with linear payoff functions. Gordon G, Dunson D, Dudík M, eds. Proc. 14th Internat. Conf. Artificial Intelligence Statistics, vol. 15 (PMLR, New York), 208–214.Google Scholar
  • Cohen MC, Lobel I, Paes Leme R (2020) Feature-based dynamic pricing. Management Sci. 66(11):4921–4943.LinkGoogle Scholar
  • Dani V, Hayes TP, Kakade SM (2008) Stochastic linear optimization under bandit feedback. Proc. 21st Conf. Learn. Theory, 355–366.Google Scholar
  • den Boer AV (2015) Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys Oper. Res. Management Sci. 20(1):1–18.CrossrefGoogle Scholar
  • Fan J, Guo Y, Yu M (2024) Policy optimization using semiparametric models for dynamic pricing. J. Amer. Statist. Assoc. 119(545):552–564.Google Scholar
  • Filippi S, Cappe O, Garivier A, Szepesvári C (2010) Parametric bandits: The generalized linear case. Proc. 24th Internat. Conf. Neural Inform. Processing Systems - Volume 1 (Curran Associates Inc., Red Hook, NY), 586–594.Google Scholar
  • Gur Y, Momeni A, Wager S (2022) Smoothness-adaptive contextual bandits. Oper. Res. 70(6):3198–3216.LinkGoogle Scholar
  • Hu Y, Kallus N, Mao X (2022) Smooth contextual bandits: Bridging the parametric and nondifferentiable regret regimes. Oper. Res. 70(6):3261–3281.LinkGoogle Scholar
  • Javanmard A, Nazerzadeh H (2019) Dynamic pricing in high-dimensions. J. Machine Learn. Res. 20(1):315–363.Google Scholar
  • Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T (2017) LightGBM: A highly efficient gradient boosting decision tree. Proc. 31st Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 3149–3157.Google Scholar
  • Keskin NB (2014) Optimal dynamic pricing with demand model uncertainty: A squared-coefficient-of-variation rule for learning and earning. Preprint, submitted November 25, https://doi.org/10.2139/ssrn.2487364.Google Scholar
  • Keskin N, Zeevi A (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167.LinkGoogle Scholar
  • Keskin NB, Li Y, Song JS (2022) Data-driven dynamic pricing and ordering with perishable inventory in a changing environment. Management Sci. 68(3):1938–1958.LinkGoogle Scholar
  • Keskin NB, Li Y, Sunar N (2025) Data-driven clustering and feature-based retail electricity pricing with smart meters. Oper. Res. 73(5):2636–2660.Google Scholar
  • Lattimore T, Szepesvári C (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).Google Scholar
  • Lei YM, Jasin S, Sinha A (2014) Near-optimal bisection search for nonparametric dynamic pricing with inventory constraint. Preprint, submitted October 15, http://dx.doi.org/10.2139/ssrn.2509425.Google Scholar
  • Li X, Zheng Z (2023) Dynamic pricing with external information and inventory constraint. Management Sci. 70(9):5985–6001.Google Scholar
  • Li L, Lu Y, Zhou D (2017) Provably optimal algorithms for generalized linear contextual bandits. Proc. 34th Internat. Conf. Machine Learn., vol. 70 (PMLR, New York), 2071–2080.Google Scholar
  • Li L, Chu W, Langford J, Moon T, Wang X (2011) An unbiased offline evaluation of contextual bandit algorithms with generalized linear models. Proc. 2011 Internat. Conf. On-Line Trading Exploration Exploitation 2, vol. 26 (JMLR.org), 19–36.Google Scholar
  • Li M, Simchi-Levi D, Tan R, Wang C, Wu MX (2023) Contextual offline demand learning and pricing with separable models. Preprint, submitted November 28, https://doi.org/10.2139/ssrn.4619018.Google Scholar
  • Luo Y, Sun WW, Liu Y (2024) Distribution-free contextual dynamic pricing. Math. Oper. Res. 49(1):599–618.LinkGoogle Scholar
  • Mao J, Leme RP, Schneider J (2018) Contextual pricing for Lipschitz buyers. Proc. 32nd Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 5648–5656.Google Scholar
  • Miao S, Wang Y (2021) Network revenue management with nonparametric demand learning: \sqrt{T}-regret and polynomial dimension dependency. Preprint, submitted October 25, https://doi.org/10.2139/ssrn.3948140.Google Scholar
  • Miao S, Chen X, Chao X, Liu J, Zhang Y (2022) Context-based dynamic pricing with online clustering. Production Oper. Management 31(9):3559–3575.Google Scholar
  • Nambiar M, Simchi-Levi D, Wang H (2019) Dynamic learning and pricing with model misspecification. Management Sci. 65(11):4980–5000.LinkGoogle Scholar
  • Neath AA, Cavanaugh JE (2012) The Bayesian information criterion: Background, derivation, and applications. Wiley Interdisciplinary Rev. Comput. Statist. 4(2):199–203.CrossrefGoogle Scholar
  • Perchet V, Rigollet P (2013) The multi-armed bandit problem with covariates. Ann. Statist. 41(2):693–721.CrossrefGoogle Scholar
  • Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) CatBoost: Unbiased boosting with categorical features. Proc. 32nd Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 6639–6649.Google Scholar
  • Qiang S, Bayati M (2016) Dynamic pricing with demand covariates. Preprint, submitted April 18, https://doi.org/10.2139/ssrn.2765257.Google Scholar
  • Rigollet P, Zeevi A (2010) Nonparametric bandits with covariates. Preprint, submitted March 8, https://arxiv.org/abs/1003.1630.Google Scholar
  • Rusmevichientong P, Tsitsiklis JN (2010) Linearly parameterized bandits. Math. Oper. Res. 35(2):395–411.LinkGoogle Scholar
  • Shah V, Johari R, Blanchet J (2019) Semi-parametric dynamic contextual pricing. Adv. Neural Inform. Processing Systems, vol. 32 (Curran Associates, Inc., Red Hook, NY), 2363–2373.Google Scholar
  • Slivkins A (2011) Contextual bandits with similarity information. Proc. 24th Ann. Conf. Learn. Theory, 679–702.Google Scholar
  • Slivkins A (2019) Introduction to multi-armed bandits. Foundations Trends® Machine Learn. 12(1–2):1–286.Google Scholar
  • Wang Y, Chen B, Simchi-Levi D (2021a) Multimodal dynamic pricing. Management Sci. 67(10):6136–6152.LinkGoogle Scholar
  • Wang Z, Deng S, Ye Y (2014) Close the gaps: A learning-while-doing algorithm for single-product revenue management problems. Oper. Res. 62(2):318–331.LinkGoogle Scholar
  • Wang H, Talluri K, Li X (2025) Technical note—On dynamic pricing with covariates. Oper. Res. 73(4):1932–1943.Google Scholar
  • Xu J, Wang YX (2021) Logarithmic regret in feature-based dynamic pricing. Proc. 35th Internat. Conf. Neural Inform. Processing Systems (Curran Associates Inc., Red Hook, NY), 13898–13910.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.