Discount targeting in online social networks using backpressure-based learning

Srinivas Shakkottai, Lei Ying

Research output: Chapter in Book/Report/Conference proceedingChapter

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 knowhow 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 multihopwireless networks),we showhowto 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.

Original languageEnglish (US)
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing
Pages427-455
Number of pages29
Volume58
DOIs
StatePublished - 2012
Externally publishedYes

Publication series

NameSpringer Optimization and Its Applications
Volume58
ISSN (Print)19316828
ISSN (Electronic)19316836

ASJC Scopus subject areas

  • Control and Optimization

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