Mining knowledge-sharing sites for viral marketing

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. Categories and Subject Descriptors H.2.8 [Database Management]: Database ...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Matthew Richardson, Pedro Domingos
• Keywords: Data Mining, KDD 2002, Keywords Probabilistic Models, Viral Marketing, Viral Marketing Plan