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.
Keywords
Viral marketingComputer scienceProbabilistic logicRobustness (evolution)Knowledge sharingKnowledge extractionData scienceKnowledge managementData miningArtificial intelligenceWorld Wide Web
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Publication Info
- Year
- 2002
- Type
- article
- Pages
- 61-70
- Citations
- 1612
- Access
- Closed
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Cite This
Matthew Richardson,
Pedro Domingos
(2002).
Mining knowledge-sharing sites for viral marketing.
, 61-70.
https://doi.org/10.1145/775047.775057
Identifiers
- DOI
- 10.1145/775047.775057