Mining knowledge-sharing sites for viral marketing

Authors:
Matthew Richardson;Pedro Domingos
Affiliations:
University of Washington, Seattle, WA;University of Washington, Seattle, WA
Venue:
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2002

Citing 11
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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.