From Hackathon to production: building a large scale recommendation engine

Could you recommend some good resources for learning more about collaborative filtering? I'd love to help hack, but feel like I should learn more first.

Most of what I learned was from Programming Collective Intelligence (http://amzn.com/0596529325). If you're in the Bay Area, Noisebridge has a self-taught machine learning class that I went to for a while (great group, I just couldn't make the time commitment).

for most of the fundamentals on Collaborative Filtering, You may check Chapters 8 and 9 from the following online book.

https://docs.google.com/viewer?url=http%3A%2F%2Finfolab.stan...

have you looked at Mahout? https://cwiki.apache.org/confluence/display/MAHOUT/Itembased...

Hm, I haven't. This suggests that Mahout might not work well for datasets on the scale we're dealing with (working toward 1 billion membership rows):

http://lucene.472066.n3.nabble.com/Mahout-not-giving-recomme...

But that's also an older article, and things might be better. Mahout might be worth checking out.

excited!