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The spread of online reviews and opinions and its growing influence onpeople's behavior and decisions, boosted the interest to extract meaningfulinformation from this data deluge. Hence, crowdsourced ratings of products andservices gained a critical role in business and governments. Currentstate-of-the-art solutions rank the items with an average of the ratingsexpressed for an item, with a consequent lack of personalization for the users,and the exposure to attacks and spamming/spurious users. Using these ratings togroup users with similar preferences might be useful to present users withitems that reflect their preferences and overcome those vulnerabilities. Inthis paper, we propose a new reputation-based ranking system, utilizingmultipartite rating subnetworks, which clusters users by their similaritiesusing three measures, two of them based on Kolmogorov complexity. We also studyits resistance to bribery and how to design optimal bribing strategies. Oursystem is novel in that it reflects the diversity of preferences by (possibly)assigning distinct rankings to the same item, for different groups of users. Weprove the convergence and efficiency of the system. By testing it on syntheticand real data, we see that it copes better with spamming/spurious users, beingmore robust to attacks than state-of-the-art approaches. Also, by clusteringusers, the effect of bribery in the proposed multipartite ranking system isdimmed, comparing to the bipartite case.

A Robust Reputation-based Group Ranking System and its Resistance to Bribery