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Mining knowledge-sharing sites for viral marketing

Published: 23 July 2002 Publication History

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.

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cover image ACM Conferences
KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
July 2002
719 pages
ISBN:158113567X
DOI:10.1145/775047
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 July 2002

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Author Tags

  1. direct marketing
  2. knowledge sharing
  3. linear models
  4. probabilistic models
  5. social networks
  6. viral marketing

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KDD '02 Paper Acceptance Rate 44 of 307 submissions, 14%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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  • (2025)Influence optimization in networks: New formulations and valid inequalitiesComputers & Operations Research10.1016/j.cor.2024.106857173(106857)Online publication date: Jan-2025
  • (2024)Viral Marketing in Social Networks with Competing ProductsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663069(2047-2056)Online publication date: 6-May-2024
  • (2024)Bookmarking Forecast and its Factor Analysis of Social Network Users Based on Gradient Boosting Decision Tree勾配ブースティング決定木に基づくソーシャルネットワーク利用者のお気に入り登録予測およびその要因分析Transactions of Japan Society of Kansei Engineering10.5057/jjske.TJSKE-D-24-00018Online publication date: 2024
  • (2024)A New Algorithm Framework for the Influence Maximization Problem Using Graph ClusteringInformation10.3390/info1502011215:2(112)Online publication date: 14-Feb-2024
  • (2024)A Survey on Event Tracking in Social Media Data StreamsBig Data Mining and Analytics10.26599/BDMA.2023.90200217:1(217-243)Online publication date: Mar-2024
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  • (2024)A General Concave Fairness Framework for Influence Maximization Based on Poverty RewardACM Transactions on Knowledge Discovery from Data10.1145/370173719:1(1-23)Online publication date: 28-Oct-2024
  • (2024)Fair Influence Maximization in HypergraphsProceedings of the Third International Workshop on Social and Metaverse Computing, Sensing and Networking10.1145/3698387.3699995(8-14)Online publication date: 4-Nov-2024
  • (2024)Fuzzy Influence Maximization in Social NetworksACM Transactions on the Web10.1145/365017918:3(1-28)Online publication date: 1-Mar-2024
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