A robust ranking algorithm to spamming

TL;DR

This paper introduces a correlation-based reputation algorithm for web rating systems that enhances robustness against spam attacks by iteratively assessing user reputation through correlation coefficients.

Contribution

The paper presents a novel reputation algorithm that improves resistance to spam in web rating systems by leveraging correlation between user ratings and object averages.

Findings

The algorithm outperforms IR and mean score methods in robustness.

It effectively detects and mitigates spammer influence.

Results validated on artificial and real datasets.

Abstract

Ranking problem of web-based rating system has attracted many attentions. A good ranking algorithm should be robust against spammer attack. Here we proposed a correlation based reputation algorithm to solve the ranking problem of such rating systems where user votes some objects with ratings. In this algorithm, reputation of user is iteratively determined by the correlation coefficient between his/her rating vector and the corresponding objects' weighted average rating vector. Comparing with iterative refinement (IR) and mean score algorithm, results for both artificial and real data indicate that, the present algorithm shows a higher robustness against spammer attack.