Discount Targeting in Online Social Networks Using Backpressure-Based Learning

Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 58)

Abstract

Online social networks are increasingly being seen as a means of obtaining awareness of user preferences. Such awareness could be used to target goods and services at them. We consider a general user model, wherein users could buy different numbers of goods at a marked and at a discounted price. Our first objective is to learn which users would be interested in a particular good. Second, we would like to know how much to discount these users such that the entire demand is realized, but not so much that profits are decreased. We develop algorithms for multihop forwarding of discount coupons over an online social network, in which users forward such coupons to each other in return for a reward. Coupling this idea with the implicit learning associated with backpressure routing (originally developed for multihop wireless networks), we show how to realize optimal revenue. Using simulations, we illustrate its superior performance as compared to random coupon forwarding on different social network topologies. We then propose a simpler heuristic algorithm and using simulations, and show that its performance approaches that of backpressure routing.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Department of Electrical and Computer EngineeringIowa State UniversityAmesUSA

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