A Robust Reputation-based Group Ranking System and its Resistance to Bribery
This addresses the problem of unreliable and impersonal online reviews for businesses and governments, offering an incremental improvement over existing methods.
The paper tackles the problem of vulnerability to attacks and lack of personalization in crowdsourced rating systems by proposing a reputation-based group ranking system that clusters users based on similarity measures, including two based on Kolmogorov complexity. The system demonstrates improved robustness to spamming and bribery compared to state-of-the-art approaches in tests on synthetic and real data.
The spread of online reviews 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 and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this paper, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case.