Difference between revisions of "Richardson and Domingos KDD 2002"
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== Summary == | == Summary == | ||
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+ | This paper attempts to show that by modeling the consumer market as a social network and exploiting the influence of peer opinions on customers, we can significantly increase profits. | ||
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* [[RelatedPapers::Leskovec et. al., ACM Conference on Electronic Commerce (EC), 2006.]] | * [[RelatedPapers::Leskovec et. al., ACM Conference on Electronic Commerce (EC), 2006.]] | ||
+ | * P. Domingos and M. Richardson. Mining the Network Valueof Customers. In Proceedings of the Seventh InternationalConference on Knowledge Discovery and Data Mining,pages 57-66, San Francisco, CA, 2001. ACM Press. | ||
== Study Plan == | == Study Plan == |
Revision as of 05:24, 27 September 2012
Contents
Citation
author = {Richardson, Matthew and Domingos, Pedro}, title = {Mining knowledge-sharing sites for viral marketing}, booktitle = {Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining}, series = {KDD '02}, year = {2002}, isbn = {1-58113-567-X}, pages = {61--70}, numpages = {10},
Online version
http://alchemy.cs.washington.edu/papers/pdfs/richardson-domingos02b.pdf
Abstract from the paper
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.
Summary
This paper attempts to show that by modeling the consumer market as a social network and exploiting the influence of peer opinions on customers, we can significantly increase profits.
Results
Related Papers
- Leskovec et. al., ACM Conference on Electronic Commerce (EC), 2006.
- P. Domingos and M. Richardson. Mining the Network Valueof Customers. In Proceedings of the Seventh InternationalConference on Knowledge Discovery and Data Mining,pages 57-66, San Francisco, CA, 2001. ACM Press.