If you just want data sets drop me a line, though most of the ones we test with are probably prohibitively large for just playing around with (millions of links

If you just want data sets drop me a line, though most of the ones we test with are probably prohibitively large for just playing around with (millions of links / ratings). O'Reilly's Programming Collective Intelligence gives some decent background information, but in my opinion doesn't really cover enough to build a real system. A couple papers I often suggest with a lot of practical content are: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions http://portal.acm.org/citation.cfm?id=1070751 http://portal.acm.org/citation.cfm?id=1070751 That one's fairly readable for people outside of the field. The Google News paper, which has some insights on doing large scale recommendations on a fairly dense user to item matrix, is a little more jumping into the deep end, but is worth glancing at even just to follow the references it sites: Google News Personalization: Scalable Online Collaborative Filtering http://www2007.org/papers/paper570.pdf http://www2007.org/papers/paper570.pdf (The paper itself doesn't mention being problematic on sparse rating sets, but I've implemented something very similar and found that to be the case.)