Advertiser Learning in Direct Advertising Markets
References
- (2023) Overwhelming targeting options: Selecting audience segments for online advertising. Internat. J. Res. Marketing 41(1):24–40.Crossref, Google Scholar
- (2025) Adjustment of bidding strategies after a switch to first-price rules. Preprint, submitted February 16, https://doi.org/10.2139/ssrn.4036006.Google Scholar
- (2017) Auctions in the online display advertising chain: A case for independent campaign management. Preprint, submitted May 19, https://doi.org/10.2139/ssrn.2919665.Google Scholar
- (2011) Position auctions with consumer search. Quart. J. Econom. 126(3):1213–1270.Crossref, Google Scholar
- (2002) Finite-time analysis of the multiarmed bandit problem. Machine Learn. 47:235–256.Crossref, Google Scholar
- (2007) Estimating dynamic models of imperfect competition. Econometrica 75(5):1331–1370.Crossref, Google Scholar
- (2017) Optimal contracts for intermediaries in online advertising. Oper. Res. 65(4):878–896.Link, Google Scholar
- (2019) Learning in repeated auctions with budgets: Regret minimization and equilibrium. Management Sci. 65(9):3952–3968.Link, Google Scholar
- (2015) Repeated auctions with budgets in ad exchanges: Approximations and design. Management Sci. 61(4):864–884.Link, Google Scholar
- (2014) Yield optimization of display advertising with ad exchange. Management Sci. 60(12):2886–2907.Link, Google Scholar
- (2000) Learning and forgetting: The dynamics of aircraft production. Amer. Econom. Rev. 90(4):1034–1054.Crossref, Google Scholar
- (1992) Estimation of a model of entry in the airline industry. Econometrica 60(4):889–917.Crossref, Google Scholar
- (2008) The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions. Transportation Res. Part B Methodological 42(3):274–303.Crossref, Google Scholar
- (2020) Managing market thickness in online business-to-business markets. Management Sci. 66(12):5783–5822.Link, Google Scholar
- (2021) Causal models for real time bidding with repeated user interactions. Zhu F, Ooi BC, Miao C, eds. Proc. 27th ACM SIGKDD Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 75–85.Google Scholar
- (2017) Real-time bidding by reinforcement learning in display advertising. de Rijke M, Shokouhi M, eds. Proc. 10th ACM Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 661–670.Google Scholar
- (2013) Learning models: An assessment of progress, challenges, and new developments. Marketing Sci. 32(6):913–938.Link, Google Scholar
- (2019) Learning in online advertising. Marketing Sci. 38(4):584–608.Link, Google Scholar
- (2020) Online display advertising markets: A literature review and future directions. Inform. Systems Res. 31(2):556–575.Link, Google Scholar
- (2005) An empirical model of advertising dynamics. Quant. Marketing Econom. 3(2):107–144.Crossref, Google Scholar
- cmdstanr: R Interface to ‘CmdStan’. R package version 0.9.0, https://mc-stan.org/cmdstanr/.Google Scholar
- (2017) A survey on solving cold start problem in recommender systems. Nand P, Johri P, eds. 2017 Internat. Conf. Comput. Comm. Automation (IEEE, Piscataway, NJ), 133–138.Google Scholar
- (2014) Small steps for workers, a giant leap for productivity. Amer. Econom. J. Appl. Econom. 6(1):73–90.Crossref, Google Scholar
- (2008) Addressing cold-start problem in recommendation systems. Kim W, Choi HJ, eds. Proc. Second Internat. Conf. Ubiquitous Inform. Management Comm. (ACM, New York), 208–211.Google Scholar
- (2014) Modeling indivisible demand. Marketing Sci. 33(3):364–381.Link, Google Scholar
- (2013) Toward an understanding of learning by doing: Evidence from an automobile assembly plant. J. Political Econom. 121(4):643–681.Crossref, Google Scholar
- (2014) Facing the cold start problem in recommender systems. Expert Systems Appl. 41(4):2065–2073.Crossref, Google Scholar
- (1969) A media planning calculus. Oper. Res. 17(1):1–35.Link, Google Scholar
- (2018) A semantic approach for estimating consumer content preferences from online search queries. Marketing Sci. 37(6):930–952.Link, Google Scholar
- (2012) Bid optimizing and inventory scoring in targeted online advertising. Yang Q, ed. Proc. 18th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 804–812.Google Scholar
- (2018) Bidding machine: Learning to bid for directly optimizing profits in display advertising. IEEE Trans. Knowledge Data Engrg. 30(4):645–659.Crossref, Google Scholar
- (2002) Methods and metrics for cold-start recommendations. Järvelin K, ed. Proc. 25th Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York, NY), 253–260.Google Scholar
- (2017) Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Sci. 36(4):500–522.Link, Google Scholar
- (2010) A modern Bayesian look at the multi-armed bandit. Appl. Stochastic Models Bus. Indust. 26(6):639–658.Crossref, Google Scholar
- (2016) tidytext: Text mining and analysis using tidy data principles in R. J. Open Source Software 1(3):37.Crossref, Google Scholar
- (2017) Penalising model component complexity: A principled, practical approach to constructing priors. Statist. Sci. 32(1):1–28.Crossref, Google Scholar
- (2023) Learning, sophistication, and the returns to advertising: Implications for differences in firm performance. NBER Working Paper No. 31201, National Bureau of Economic Research, Cambridge, MA.Google Scholar
- (2017) Idea generation, creativity, and prototypicality. Marketing Sci. 36(1):1–20.Link, Google Scholar
- (2022) Display ad measurement using observational data: A reinforcement learning approach. Unpublished PhD thesis, University of Wisconsin–Madison, Madison, WI.Google Scholar
- (2020) A near-optimal bidding strategy for real-time display advertising auctions. J. Marketing Res. 58(1):1–21.Crossref, Google Scholar
- (2007) Position auctions. Internat. J. Indust. Organ. 25(6):1163–1178.Crossref, Google Scholar
- (2016) Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statist. Comput. 27(5):1413–1432.Crossref, Google Scholar
- (2025) Online causal inference for advertising in real-time bidding Auctions. Marketing Sci. 44(1):176–195. Link, Google Scholar
- (2025) Recommending for a multi-sided marketplace: A multi-objective hierarchical approach. Marketing Sci. 44(1):1–29.Link, Google Scholar
- (2015) Matching value and market design in online advertising networks: An empirical analysis. Marketing Sci. 34(6):906–921.Link, Google Scholar
- (2023) A scalable recommendation engine for new users and items. Preprint, submitted September 15, https://doi.org/10.2139/ssrn.4202543.Google Scholar
- (2023) Multi-view multi-task campaign embedding for cold-start conversion rate forecasting. IEEE Trans. Big Data 9(1):280–293.Crossref, Google Scholar
- (2023) Exploit or explore? An empirical study of resource allocation in scientific labs. Working paper, Harvard University, Cambridge, MA.Google Scholar

