We tried using it a while ago when it was first getting started. It was a pain to use back then but afaik it has advanced quite a bit and after seeing this post I'll be taking another jab at it. If you want an out-of-the-box recommender, Mahout is good while providing many other Machine Learning algorithms that can be run at scale.

You definitely need to have some technical knowledge though. One thing that I don't like about things like this is that if there is a bug and you don't understand the software (or the math) you won't be able to troubleshoot.

This is why I tend to write my own for the specific task at hand.

Agreed, it has a very steep learning curve. Once you understand it (and understand how to think in map-reduce), it's pretty awesome -- and easy to extend. Rolling your own definitely makes the learning curve easier, but you can miss out on some of the M-R efficient algorithms that are built in it.

I am currently using Mahout at work for k-means clustering and singular value decomposition. It seems to be working well.

My team at Amazon is using it. There's also been some talks given on it at Amazon.