  Rating platforms enable large-scale collection of user opinion about items
(products, other users, etc.). However, many untrustworthy users give
fraudulent ratings for excessive monetary gains. In the paper, we present
FairJudge, a system to identify such fraudulent users. We propose three
metrics: (i) the fairness of a user that quantifies how trustworthy the user is
in rating the products, (ii) the reliability of a rating that measures how
reliable the rating is, and (iii) the goodness of a product that measures the
quality of the product. Intuitively, a user is fair if it provides reliable
ratings that are close to the goodness of the product. We formulate a mutually
recursive definition of these metrics, and further address cold start problems
and incorporate behavioral properties of users and products in the formulation.
We propose an iterative algorithm, FairJudge, to predict the values of the
three metrics. We prove that FairJudge is guaranteed to converge in a bounded
number of iterations, with linear time complexity. By conducting five different
experiments on five rating platforms, we show that FairJudge significantly
outperforms nine existing algorithms in predicting fair and unfair users. We
reported the 100 most unfair users in the Flipkart network to their review
fraud investigators, and 80 users were correctly identified (80% accuracy). The
FairJudge algorithm is already being deployed at Flipkart.
