(Submitted on 13 Apr 2020)

Abstract: 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.