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Mining knowledge-sharing sites for viral marketing

Mining knowledge-sharing sites for viral marketing Mining Knowledge-Sharing Sites for Viral Marketing Matthew Richardson and Pedro Domingos Department of Computer Science and Engineering University of Washington Box 352350 Seattle, WA 98195-2350 {mattr, pedrod}@cs.washington.edu ABSTRACT Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in' computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach. ships between people makes viral marketing potentially more profitable than direct marketing. Data mining techniques have been successfully employed for http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Mining knowledge-sharing sites for viral marketing

Association for Computing Machinery — Jul 23, 2002

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References (26)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2002 by ACM Inc.
ISBN
1-58113-567-X
doi
10.1145/775047.775057
Publisher site
See Article on Publisher Site

Abstract

Mining Knowledge-Sharing Sites for Viral Marketing Matthew Richardson and Pedro Domingos Department of Computer Science and Engineering University of Washington Box 352350 Seattle, WA 98195-2350 {mattr, pedrod}@cs.washington.edu ABSTRACT Viral marketing takes advantage of networks of influence among customers to inexpensively achieve large changes in behavior. Our research seeks to put it on a firmer footing by mining these networks from data, building probabilistic models of them, and using these models to choose the best viral marketing plan. Knowledge-sharing sites, where customers review products and advise each other, are a fertile source for this type of data mining. In this paper we extend our previous techniques, achieving a large reduction in' computational cost, and apply them to data from a knowledge-sharing site. We optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him. We take into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost. Our results show the robustness and utility of our approach. ships between people makes viral marketing potentially more profitable than direct marketing. Data mining techniques have been successfully employed for

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