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An Online Algorithm for Learning Buyer Behavior under Realistic Pricing Restrictions

Debjyoti Saharoy, Theja Tulabandhula

arXiv 6 Mar 2018 · Statistics — Machine Learning

arXiv:1803.01968 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose a new efficient online algorithm to learn the parameters governing the purchasing behavior of a utility maximizing buyer, who responds to prices, in a repeated interaction setting. The key feature of our algorithm is that it can learn even non-linear buyer utility while working with arbitrary price constraints that the seller may impose. This overcomes a major shortcoming of previous approaches, which use unrealistic prices to learn these parameters making them unsuitable in practice.

Citation extraction

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Maxime Cohen, Ilan Lobel, and Renato Paes Leme (2016) Feature-based dynamic pricing0.87472100%
2Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, and Vija… (2014) Learning economic parameters from revealed preferences0.73732100%
3Aaron Roth, Jonathan Ullman, and Zhiwei Steven Wu (2016) Watch and learn: Optimizing from revealed preferences feedback0.64441100%
4Benjamin Letham, Lydia M. Letham, and Cynthia Rudin (2016) Bayesian inference of arrival rate and substitution behavior from sales transaction data with stockouts0.51121100%
5Martin Grötschel, László Lovász, and Alexander Schrijver (2012) Geometric Algorithms and Combinatorial Optimization, volume 20.51121100%
6Saeed Alaei (2011) Bayesian combinatorial auctions: Expanding single buyer mechanisms to many buyers0.40511100%
7Kareem Amin, Afshin Rostamizadeh, and Umar Syed (2014) Repeated contextual auctions with strategic buyers0.40511100%
bazaraa2013nonlinearunmatched citation key bazaraa2013nonlinear0.40511100%
9Xiaohui Bei, Wei Chen, Jugal Garg, Martin Hoefer, and Xiaoming Sun (2016) Learning market parameters using aggregate demand queries0.40511100%
10Eyal Beigman and Rakesh Vohra (2006) Learning from revealed preference0.40511100%

Showing the top 10 of 18 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.