REV2: Fraudulent user prediction in Rating platforms

Rating platforms enable large-scale collection of user opinion about items (e.g., products or other users). However, fraudulent users give fake ratings for excessive monetary gains. In this paper, we present Rev2, a system to identify such fraudulent users. We propose three interdependent intrinsic quality metrics-fairness of a user, reliability of a rating and goodness of a product. The fairness and reliability quantify the trustworthiness of a user and rating, respectively, and goodness quantifies the quality of a product. Intuitively, a user is fair if it provides reliable scores that are close to the goodness of products. We propose six axioms to establish the interdependency between the scores, and then, formulate a mutually recursive definition that satisfies these axioms. We extend the formulation to address cold start problem and incorporate behavior properties. We develop the Rev2 algorithm to calculate these intrinsic scores for all users, ratings, and products by combining network and behavior properties. We prove that this algorithm is guaranteed to converge and has linear time complexity. By conducting extensive experiments on five rating datasets, we show that Rev2 outperforms nine existing algorithms in detecting fraudulent users. We reported the 150 most unfair users in the Flipkart network to their review fraud investigators, and 127 users were identified as being fraudulent (84.6% accuracy). The Rev2 algorithm is being deployed at Flipkart.