Performance of the Toronto Paper Matching System (TPMS) for (conference) paper review assignment [EDIT (2016-11-11) based on a comment]

I have been invited as a reviewer for the IEEE International Conference on Image Processing  (ICIP 2016, on image processing, in a broad domain sometimes associated to computer science). The ancient system was based on ticked keywords (by benevolent reviewers) in a list (and on the area chairs' expertise). 

The novel system is called TPMS. It is based on the paper Toronto Paper Matching System: An automated paper-reviewer assignment system. It uses a machine learning ("statistical matching") technique based on a sample of the reviewer's publications. It has also been used for CVPR 2016 (Computer Vision and Pattern Recognition conference).


Has the TPMS system   been successfully deployed?
Do reviewers or editors have a quantitative evaluation  of the efficiency of such systems?
Laurent Charlin's reference (http://papermatching.cs.toronto.edu/webapp/profileBrowser/about_us/) suggests that it's been adopted by "over 50 conferences", including the following conferences:


European Conference on Computer Vision
Conference on Uncertainty in Artificial Intelligence 
Conference on Neural Information Processing Systems
Conference on Computer Vision and Pattern Recognition
International Conference on Computer Vision
International Conference on Machine Learning
Artificial Intelligence and Statistics Conference 


And it also "keeps the profiles of 8,000 reviewers". In machine learning circles, this is just about as good as it gets with non-mass-market data. The size of the training pool and the fact that it's been continuously worked on means it is likely to have become a fairly reliable tool.