| Sorry for the delay, I just now saw that somebody had replied. Regarding links to the systems- the only one that was ever publicly accessible was for a Facebook app that I wrote some years back that was let researchers post links to their publications on their profiles, and also featured a recommendation system to find other users of the app that had "similar" publications. It's unfortunately fallen into a pretty sorry state of disrepair, as I haven't had any time to devote to maintenance in more than two years and Facebook's APIs have changed quite a bit since then. I described the underlying system a little bit in a conference paper: http://view.ncbi.nlm.nih.gov/pubmed/18999247 The other systems I've used the approach for have all been along either bibliometric/bibliographic lines, or have been relating to content-based image retrieval. It's a pretty robust approach, but can take a little bit of tuning to get just right- coming up with a good evaluation strategy is important to getting the most out of it, I've found. As far as references that were useful: Ilya Grigorik has a very accessible getting-your-feet-wet tutorial on his site: http://www.igvita.com/2007/01/15/svd-recommendation-system-i... It might be a little dated w.r.t. specific libraries or APIs, but the basic technique is there. For a more comprehensive look at the SVD-IR approach, take a look at: Berry et al. Using Linear Algebra for Intelligent Information Retrieval. SIAM Review (1995) vol. 37 (4) pp. 573-595 The SVD approach falls in the same family as Latent Semantic Analysis, which is a whole black art unto itself- I'd actually suggest going back to the early papers by Landauer, Dumais, etc. if you're really interested- those guys did a great job writing up what was at the time really novel stuff. My contact info should be in my profile, drop me an email if you have any more questions (or to let me know what you end up doing!). |