Recommendation Engine by Ricky Ho.
From the post:
In a classical model of recommendation system, there are “users” and “items”. User has associated metadata (or content) such as age, gender, race and other demographic information. Items also has its metadata such as text description, price, weight … etc. On top of that, there are interaction (or transaction) between user and items, such as userA download/purchase movieB, userX give a rating 5 to productY … etc.
Ricky does a good job of stepping through the different approaches to making recommendations. Iimportant for topic map interfaces that recommend additional topics to their users.