When I graduated college, most of this work was being done in graduate research centers and for the defense department. The great thing about the Web is that it opened up many new problems for computer science. We are in the information age. Our problem isn't getting more information to us, but wading through it all to find out what we really care about. The Internet in bringing tons of information to the masses, renews the problem of how do we facilitate matching content and users.
Relevance is a very interesting project involving both automated learning and recognition.
Our technology essentially puts users and documents together by understanding them both at a conceptual level. This technique has an advantage over statistical methods such as collaborative filtering in that you don't have to go through so much training with your users to get good results, and you can serve individuals on the basis of their individual needs. From an implementation standpoint, there are many challenges. First of all, this is a huge problem from a processing standpoint. Let's say you have a million users, and you want to classify 100,000 documents.