Mining knowledge-sharing sites for viral marketing

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Mining knowledge-sharing sites for viral marketing

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

Edmonton, Alberta, Canada

SESSION: Graphs and trees table of contents

Pages: 61 - 70

Year of Publication: 2002

ISBN:1-58113-567-X

Authors

Matthew Richardson	University of Washington, Seattle, WA
Pedro Domingos	University of Washington, Seattle, WA

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
SIGMOD: ACM Special Interest Group on Management of Data
: AAAI

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 49, Downloads (12 Months): 440, Citation Count: 48

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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.

REFERENCES

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