Mining collective intelligence in diverse groups

Guo Jun Qi, Charu C. Aggarwal, Jiawei Han, Thomas Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Collective intelligence, which aggregates the shared information from large crowds, is often negatively impacted by unreliable information sources with the low quality data. This becomes a barrier to the effective use of collective intelligence in a variety of applications. In order to address this issue, we propose a probabilistic model to jointly assess the reliability of sources and find the true data. We observe that different sources are often not independent of each other. Instead, sources are prone to be mutually influenced, which makes them dependent when sharing information with each other. High dependency between sources makes collective intelligence vulnerable to the overuse of redundant (and possibly incorrect) information from the dependent sources. Thus, we reveal the latent group structure among dependent sources, and aggregate the information at the group level rather than from individual sources directly. This can prevent the collective intelligence from being inappropriately dominated by dependent sources. We will also explicitly reveal the reliability of groups, and minimize the negative impacts of unreliable groups. Experimental results on real-world data sets show the effectiveness of the proposed approach with respect to existing algorithms. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Original languageEnglish (US)
Title of host publicationWWW 2013 - Proceedings of the 22nd International Conference on World Wide Web
PublisherAssociation for Computing Machinery
Pages1041-1051
Number of pages11
ISBN (Print)9781450320351
DOIs
StatePublished - 2013
Event22nd International Conference on World Wide Web, WWW 2013 - Rio de Janeiro, Brazil
Duration: May 13 2013May 17 2013

Publication series

NameWWW 2013 - Proceedings of the 22nd International Conference on World Wide Web

Other

Other22nd International Conference on World Wide Web, WWW 2013
CountryBrazil
CityRio de Janeiro
Period5/13/135/17/13

Keywords

  • Collective intelligence
  • Crowd sourcing
  • Robust classifier

ASJC Scopus subject areas

  • Computer Networks and Communications

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