2022
DOI: 10.1287/mnsc.2021.4246
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Joint Learning and Optimization for Multi-Product Pricing (and Ranking) Under a General Cascade Click Model

Abstract: We consider joint learning and optimization problems under a general Cascade Click model. Under this model, customers examine the products in a decreasing order of display, from the top to (potentially) the bottom of the list. At each step, customers can decide to either purchase the current product, forego the current product and continue examining the next product, or simply terminate the search without purchasing any product. We first consider the core pricing problem, where the display position (ranking) o… Show more

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Cited by 16 publications
(5 citation statements)
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References 39 publications
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“…The proposed algorithm iteratively adds a product to the current list, experiments with the display to obtain response data, and then decides, based on a threshold response rate, whether or not to keep the newly added product on the list. In a related context, Gao et al (2022) consider a cascade-click model for the consumer purchase choice. They develop contextual-based (i.e., click-based) upper confidence bounds for dynamic learning and price optimization.…”
Section: Choice-based Product Assortment Display and Pricingmentioning
confidence: 99%
See 1 more Smart Citation
“…The proposed algorithm iteratively adds a product to the current list, experiments with the display to obtain response data, and then decides, based on a threshold response rate, whether or not to keep the newly added product on the list. In a related context, Gao et al (2022) consider a cascade-click model for the consumer purchase choice. They develop contextual-based (i.e., click-based) upper confidence bounds for dynamic learning and price optimization.…”
Section: Choice-based Product Assortment Display and Pricingmentioning
confidence: 99%
“…In a related context, Gao et al. (2022) consider a cascade‐click model for the consumer purchase choice. They develop contextual‐based (i.e., click‐based) upper confidence bounds for dynamic learning and price optimization.…”
Section: Choice Options and Pricingmentioning
confidence: 99%
“…These models are also closely related to the cascade model [12,36,38,54,19]. Gao et al [32] further propose the general cascade click models. Following these works, we suppose that each stage contains only one product and the consumers view the products sequentially.…”
Section: Related Workmentioning
confidence: 99%
“…The consumer choice model, when faced with a list of products, is the basis of the problem [17]. Generally speaking, existing works assume that the consumers view the list of products sequentially and select products according to a choice model [43,42,32,38,18,26,25,37]. However, most of them assume that each consumer purchases at most one product and stop browsing immediately afterward, which does not agree with the common practice since a consumer may expect multiple purchases on a website or an app [34,35,16,28].…”
Section: Introductionmentioning
confidence: 99%
“…In contrast, because we focus on revenue maximization, the target policy doesn't have a structure to facilitate learning. Gao et al (2018) investigate the optimal pricing of the cascade model, in which the clicking and purchasing probabilities are parametrized and need to be learned. Since their major focus is pricing instead of ranking, the design of the algorithm deviates signi cantly from ours.…”
Section: Online Learning and Multi-armed Banditmentioning
confidence: 99%