Paper Info
Title
FairJudge: Trustworthy User Prediction in Rating Platforms.
Abstract
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.
Year
Venue
Field
2017
arXiv: Social and Information Networks
Data mining,Trustworthiness,Computer science,Iterative method,Artificial intelligence,Time complexity,Cold start (automotive),Machine learning,Bounded function,Recursive definition
DocType
Volume
Citations 
Journal
abs/1703.10545
3
PageRank 
References 
Authors
0.37
24
6
Name
Order
Citations
PageRank
Srijan Kumar132624.97
bryan hooi228728.70
disha makhija3591.96
mohit kumar4693.19
Christos Faloutsos5279724490.38
V. S. Subrahmanian668641053.38