TY - GEN
T1 - Mining collective intelligence in diverse groups
AU - Qi, Guo Jun
AU - Aggarwal, Charu C.
AU - Han, Jiawei
AU - Huang, Thomas
PY - 2013
Y1 - 2013
N2 - 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).
AB - 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).
KW - Collective intelligence
KW - Crowd sourcing
KW - Robust classifier
UR - http://www.scopus.com/inward/record.url?scp=84892655060&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84892655060&partnerID=8YFLogxK
U2 - 10.1145/2488388.2488479
DO - 10.1145/2488388.2488479
M3 - Conference contribution
AN - SCOPUS:84892655060
SN - 9781450320351
T3 - WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web
SP - 1041
EP - 1051
BT - WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web
PB - Association for Computing Machinery
T2 - 22nd International Conference on World Wide Web, WWW 2013
Y2 - 13 May 2013 through 17 May 2013
ER -