  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.
