Revenue Maximization and Learning in Product Ranking
References
- (2016) Assortment optimization under a multinomial logit model with position bias and social influence. 4OR 14(1):57–75.Crossref, Google Scholar
- (2011) Location, location, location: An analysis of profitability of position in online advertising markets. J. Marketing Res. 48(6):1057–1073.Crossref, Google Scholar
- (2019) MNL-bandit: A dynamic learning approach to assortment selection. Oper. Res. 67(5):1453–1485.Link, Google Scholar
- (2020) Display optimization for vertically differentiated locations under multinomial logit preferences. Management Sci. 67(6):3519–3550.Link, Google Scholar
- (2025) The click-based MNL model: A framework for modeling click data in assortment optimization. Management Sci. 71(8):6943–6960.Link, Google Scholar
- (2009) Dynamic pricing for nonperishable products with demand learning. Oper. Res. 57(5):1169–1188.Link, Google Scholar
- (2023) Sequential submodular maximization and applications to ranking an assortment of products. Oper. Res. 71(4):1154–1170.Link, Google Scholar
- (2009) Clicks, discontinuities, and firm demand online. J. Econom. Management Strategy 18(4):935–975.Crossref, Google Scholar
- (2009) Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res. 57(6):1407–1420.Link, Google Scholar
- (2012) Blind network revenue management. Oper. Res. 60(6):1537–1550.Link, Google Scholar
- (2015) Non-stationary stochastic optimization. Oper. Res. 63(5):1227–1244.Link, Google Scholar
- (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980.Link, Google Scholar
- (2025) Online matching frameworks under stochastic rewards, product ranking, and unknown patience. Oper. Res. 73(2):995–1010.Link, Google Scholar
- (2012) Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations Trends Machine Learn. 5(1):1–122.Crossref, Google Scholar
- (2019) Dynamic learning of sequential choice bandit problem under marketing fatigue. Proc. AAAI Conf. Artificial Intelligence 33(1):3264–3271.Crossref, Google Scholar
- (2019) Doubly adaptive cascading bandits with user abandonment. Working paper, University of Texas, Austin.Google Scholar
- (2011) Search and satisficing. Amer. Econom. Rev. 101(7):2899–2922.Crossref, Google Scholar
- (2021) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.Link, Google Scholar
- (2022) A primal-dual learning algorithm for personalized dynamic pricing with an inventory constraint. Math. Oper. Res. 47(4):2585–2613.Link, Google Scholar
- (2019) Nonparametric self-adjusting control for joint learning and optimization of multiproduct pricing with finite resource capacity. Math. Oper. Res. 44(2):601–631.Link, Google Scholar
- (2020) Dynamic assortment optimization with changing contextual information. J. Machine Learn. Res. 21(216):1–44.Google Scholar
- (2025) Position auctions with endogenous product information: Why live-streaming advertising is thriving. Management Sci. 71(11):9290–9307.Link, Google Scholar
- (2017) Dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6):1722–1731.Link, Google Scholar
- (2019) A Thompson sampling algorithm for cascading bandits. Chaudhuri K, Sugiyama M, eds. Proc. 22nd Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 438–447.Google Scholar
- (2008) An experimental comparison of click position-bias models. Proc. 2008 Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 87–94.Google Scholar
- (2015) Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys Oper. Res. Management Sci. 20(1):1–18.Crossref, Google Scholar
- (2022) Product ranking on online platforms. Management Sci. 68(6):4024–4041.Link, Google Scholar
- (2019) Improved approximation schemes for MNL-driven sequential assortment optimization. Working paper, Washington University, St. Louis.Google Scholar
- (2007) Implementing sponsored search in web search engines: Computational evaluation of alternative mechanisms. INFORMS J. Comput. 19(1):137–148.Link, Google Scholar
- (2022) Learning to rank an assortment of products. Management Sci. 68(3):1828–1848.Link, Google Scholar
- (2018) Online network revenue management using Thompson sampling. Oper. Res. 66(6):1586–1602.Link, Google Scholar
- (2019) Assortment optimization under the sequential multinomial logit model. Eur. J. Oper. Res. 273(3):1052 –1064.Crossref, Google Scholar
- (2024) A random consideration set model for demand estimation, assortment optimization, and pricing. Oper Res. 72(6):2358–2374.Link, Google Scholar
- (2020) Approximation algorithms for product framing and pricing. Oper. Res. 68(1):134–160.Link, Google Scholar
- (2022) Joint learning and optimization for multi-product pricing (and ranking) under a general cascade click model. Management Sci. 68(10):7362–7382.Link, Google Scholar
- (2021) Assortment optimization and pricing under the multinomial logit model with impatient customers: Sequential recommendation and selection. Oper. Res. 69(5):1509–1532.Link, Google Scholar
- Gah-Yi B, Bora KN (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.Google Scholar
- (2009) An empirical analysis of search engine advertising: Sponsored search in electronic markets. Management Sci. 55(10):1605–1622.Link, Google Scholar
- (2022) Learning product rankings robust to fake users. Oper. Res. 71(4):1171–1196.Link, Google Scholar
- (2020) Dynamic assortment personalization in high dimensions. Oper. Res. 68(4):1020–1037.Link, Google Scholar
- (2016) DCM bandits: Learning to rank with multiple clicks. Balcan MF, Weinberger KQ, eds. Proc. 33rd Internat. Conf. Machine Learn. (PMLR, New York), 1215–1224.Google Scholar
- (2008) A cascade model for externalities in sponsored search. Papadimitriou C, Zhang S, eds. Internet Network Econom. WINE 2008 (Springer, Berlin), 585–596.Google Scholar
- (2015a) Cascading bandits: Learning to rank in the cascade model. Bach F, Blei D, eds. Proc. 32nd Internat. Conf. Machine Learn., vol. 37 (PMLR, New York), 767–776.Google Scholar
- (2015b) Combinatorial cascading bandits. Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 28 (NIPS 2015) (Curran Associates Inc., Red Hook, NY), 1450–1458.Google Scholar
- (2016) Multiple-play bandits in the position-based model. Lee D, Sugiyama M, Luxburg U, Guyon I, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 29 (NIPS 2016) (Curran Associates Inc., Red Hook, NY), 1597–1605.Google Scholar
- (2020) Assortment optimization under the multinomial logit model with sequential offerings. INFORMS J. Comput. 32(3):835–853.Link, Google Scholar
- (2001) Stocking retail assortments under dynamic consumer substitution. Oper. Res. 49(3):334–351.Link, Google Scholar
- (2022) Context-based dynamic pricing with online clustering. Production Oper. Management 31(9):3559–3575.Crossref, Google Scholar
- (2021) Online learning via offline greedy algorithms: Applications in market design and optimization. Proc. 22nd ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 737–738.Google Scholar
- (2019) Thompson sampling for multinomial logit contextual bandits. Wallach H, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox E, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 32 (Curran Associates Inc., Red Hook, NY), 3151–3161.Google Scholar
- (2014) The Hazard Rate: Theory and Inference (with Supplementary MATLAB-Programs) (Justus-Liebig-University, Giessen, Germany).Google Scholar
- (2010) Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Oper. Res. 58(6):1666–1680.Link, Google Scholar
- (2013) Optimal dynamic assortment planning with demand learning. Manufacturing Service Oper. Management 15(3):387–404.Link, Google Scholar
- (1955) A behavioral model of rational choice. Quart. J. Econom. 69(1):99–118.Crossref, Google Scholar
- (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33.Link, Google Scholar
- (2018) The impact of consumer search cost on assortment planning and pricing. Management Sci. 64(8):3649–3666.Link, Google Scholar
- (2024) Inventory Ordering and Product Ranking for Online Curation Retailers. Working paper, Boston College, Chestnut Hill, MA.Google Scholar
- (2017) Online learning to rank in stochastic click models. Precup D, Teh YW, eds. Proc. 34th Internat. Conf. Machine Learn. (PMLR, New York), 4199–4208.Google Scholar
- (2016) Cascading bandits for large-scale recommendation problems. Ihler A, Janzing D, eds. UAI’16 Proc. 32nd Conf. Uncertainty Artificial Intelligence (AUAI Press, Arlington, VA), 835–844.Google Scholar

