  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.
