# Using collaborative filtering to “clean” data and the other way around

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.

Some references that might interest you:
1] Authoritative sources in a hyperlinked environment
Jon Kleinberg
JACM 1999 http://www.cs.cornell.edu/home/kleinber/auth.pdf

2] Truth discovery with multiple conflicting information providers on the web
Xiaoxin Yin, Jiawei Han, Philip Yu
KDD 2007 http://www.cs.uiuc.edu/~hanj/pdf/kdd07_xyin.pdf

3] A Bayesian approach to discovering truth from conflicting sources for data integration
Bo Zhao, Benjamin Rubinstein, Jim Gemmel, Jiawei Han
VLDB 2012 http://vldb.org/pvldb/vol5/p550_bozhao_vldb2012.pdf

If you check the references and look at papers that cite these papers, you should get lot more papers along these lines.