A Robust Reputation-Based Group Ranking System and Its Resistance to Bribery

The spread of online reviews and opinions its growing influence on people’s behavior decisions boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings products services gained a critical role in business governments. Current state-of-the-art solutions rank items with an average expressed for item, consequent lack personalization users, exposure attacks spamming/spurious users. Using these group users similar preferences might be useful present that reflect their overcome those vulnerabilities. In article, we propose new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters by similarities using three measures, two them based Kolmogorov complexity. We also study resistance bribery how design optimal bribing strategies. Our system is novel it reflects diversity (possibly) assigning distinct rankings same different groups prove convergence efficiency system. By testing synthetic real data, see copes better being more robust than approaches. Also, clustering effect proposed dimmed, comparing bipartite case.