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 article, 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.

We study bribery resistance properties in two classes of reputation-based ranking systems, where the rankings are computed by weighting the rates given by users with their reputations. In the first class, the rankings are the result of the aggregation of all the ratings, and all users are provided with the same ranking for each item. In the second class, there is a first step that clusters users by their rating pattern similarities, and then the rankings are computed cluster-wise. Hence, for each item, there is a different ranking for distinct clusters. We study the setting where the seller of each item can bribe users to rate the item, if they did not rate it before, or to increase their previous rating on the item. We model bribing strategies under these ranking scenarios and explore under which conditions it is profitable to bribe a user, presenting, in several cases, the optimal bribing strategies. By computing dedicated rankings to each cluster, we show that bribing, in general, is not as profitable as in the simpler without clustering. Finally, we illustrate our results with experiments using real data.