Difference between revisions of "Richardson and Domingos KDD 2002"
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year = {2002}, | year = {2002}, | ||
isbn = {1-58113-567-X}, | isbn = {1-58113-567-X}, | ||
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pages = {61--70}, | pages = {61--70}, | ||
numpages = {10}, | numpages = {10}, | ||
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| + | == Online version == | ||
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| + | http://alchemy.cs.washington.edu/papers/pdfs/richardson-domingos02b.pdf | ||
== Abstract from the paper == | == Abstract from the paper == | ||
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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 | 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. | 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. | ||
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| + | == Summary == | ||
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| + | == Study Plan == | ||
Revision as of 06:01, 27 September 2012
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.
