I am considering two types of systems - which might have more appropriate names:
Recommender systems: These recommender systems are based on collaborative filtering methods, both model-based and memory-based. They take a partially filled user/item matrix of ratings of items by users, and try to predict the missing ratings.
Trust systems: These systems take a filled user/item matrix of ratings, and try to detect a trustworthiness for each user - which can then be used to give more fair ratings. These systems could for example be used to eliminate overly negative users or fake ratings.
There seems to be a lot of overlap between these two types of systems. My question is, if it is possible to simulate one system using the other. For example - to simulate a trust system with a recommender system - one could remove a rating $r$, let the recommender system predict it, and see how big a difference the actual and predicted value have.
Is it possible to use such a trick? What is the correct terminology for these systems?
I am most interested in systems of the second type, but have so far stumbled upon most research papers on the first type.