Building reputation systems for better ranking

TL;DR

This paper introduces an iterative algorithm for ranking objects and evaluating user reputation in rating systems, significantly improving accuracy on real and synthetic data, and emphasizing the importance of reputation systems online.

Contribution

It presents a novel iterative method that jointly assesses user reputation and object quality, advancing ranking accuracy in rating systems.

Findings

Algorithm improves ranking accuracy on real datasets

Joint evaluation of user reputation and object quality is effective

Reputation systems are crucial for online ranking performance

Abstract

How to rank web pages, scientists and online resources has recently attracted increasing attention from both physicists and computer scientists. In this paper, we study the ranking problem of rating systems where users vote objects by discrete ratings. We propose an algorithm that can simultaneously evaluate the user reputation and object quality in an iterative refinement way. According to both the artificially generated data and the real data from MovieLens and Amazon, our algorithm can considerably enhance the ranking accuracy. This work highlights the significance of reputation systems in the Internet era and points out a way to evaluate and compare the performances of different reputation systems.