Emily is a semantic recommendation system for blogs that I've been working on. If you give it an Atom or RSS feed from a blog, it will create a feed of items from other blogs that hopefully match your interests.
It does this by using significant associations between words to infer your interests. Suppose a randomly-chosen sentence from your blog has a probability P(A) of containing word A, and a probability P(B) of containing word B. If there were no relationship between the words, we would expect the probability of a sentence containing both words to be P(AB)=P(A)P(B). If there is significant information contained in the relationship between the words, they will cooccur more frequently than this, and we can quantify this with an entropy, H=log2 P(AB) - log2 P(A) - log2 P(B)
Emily uses the strengths of these associations to calculate the similarity between two blogs. Then, if you post an article that makes your blog more similar to somebody else's blog than it was before, that article is recommended to them.
This has been an interesting project for me. I've learned about Google App Engine, pubsubhubbub and Atom. What I need now is for people to try it out. I'm looking forward to when Emily starts finding things for me.