Robust reputation-based ranking on multipartite rating networks

TL;DR

This paper introduces a reputation-based ranking system for multipartite rating networks that clusters users by similarity, providing diverse item rankings and demonstrating robustness against spam and attacks.

Contribution

It presents a novel ranking method using Kolmogorov complexity to cluster users and produce diverse, robust rankings for different user groups.

Findings

System converges efficiently and reliably.

Outperforms existing methods in resisting spam and malicious attacks.

Provides diverse rankings reflecting user preferences.

Abstract

The spread of online reviews, ratings and opinions and its growing influence on people's behavior and decisions boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business, governments, and others. We propose a new reputation-based ranking system utilizing multipartite rating subnetworks, that clusters users by their similarities, using Kolmogorov complexity. Our system is novel in that it reflects a diversity of opinions/preferences by assigning possibly distinct rankings, for the same item, for different groups of users. We prove the convergence and efficiency of the system and show that it copes better with spamming/spurious users, and it is more robust to attacks than state-of-the-art approaches.