  In this paper we introduce an iterative voting algorithm and then use it to
obtain a rating method which is very robust against collusion attacks as well
as random and biased raters. Unlike the previous iterative methods, our method
is not based on comparing submitted evaluations to an approximation of the
final rating scores, and it entirely decouples credibility assessment of the
cast evaluations from the ranking itself. The convergence of our algorithm
relies on the existence of a fixed point of a continuous mapping which is also
a stationary point of a constrained optimization objective. We have implemented
and tested our rating method using both simulated data as well as real world
data. In particular, we have applied our method to movie evaluations obtained
from MovieLens and compared our results with IMDb and Rotten Tomatoes movie
rating sites. Not only are the ratings provided by our system very close to
IMDb rating scores, but when we differ from the IMDb ratings, the direction of
such differences is essentially always towards the ratings provided by the
critics in Rotten Tomatoes. Our tests demonstrate high efficiency of our
method, especially for very large online rating systems, for which trust
management is both of the highest importance and one of the most challenging
problems.
