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Algorithms for Multi-product Pricing

  • Gagan Aggarwal
  • Tomás Feder
  • Rajeev Motwani
  • An Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3142)

Abstract

In the information age, the availability of data on consumer profiles has opened new possibilities for companies to increase their revenue via data mining techniques. One approach has been to strategically set prices of various products, taking into account the profiles of consumers. We study algorithms for the multi-product pricing problem, where, given consumer preferences among products, their budgets, and the costs of production, the goal is to set prices of multiple products from a single company, so as to maximize the overall revenue of the company. We present approximation algorithms as well as negative results for several variants of the multi-product pricing problem, modeling different purchasing patterns and market assumptions.

Keywords

Approximation Factor Total Payoff Revenue Management Preference List Maximum Revenue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gagan Aggarwal
    • 1
  • Tomás Feder
    • 1
  • Rajeev Motwani
    • 1
  • An Zhu
    • 1
  1. 1.Computer Science DepartmentStanford UniversityStanfordUSA

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