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Since I work with recommender systems, I’d hardly be doing my job if I didn’t notice things like Google Reader’s new feed recommendations. From the description of how the recommender works on the Google help page (which is unfortunately not very specific):
Your recommendations list is automatically generated. It takes into account the feeds you’re already subscribed to, as well as information from your Web History, including your location. Aggregated across many users, this information can indicate which feeds are popular among people with similar interests. For instance, if a lot of people subscribe to feeds about both peanut butter and jelly, and you only subscribe to feeds about peanut butter, Reader will recommend that you try some jelly.
This sounds like they are using a hybrid recommender system. When you are recommending items (in this case feeds) to users, you can either consider the qualities of the items themselves (content-based) or the behavior of people similar to you (collaborative filtering). The Netflix Prize is a collaborative filtering case for the most part, though it is possible to add in some amount of content.
You Belong in London |
 A little old fashioned, and a little modern.A little traditional, and a little bit punk rock. A unique soul like you needs a city that offers everything. No wonder you and London will get along so well. |
This, via my friend Israel. I guess we both belong there. Let me know what your results are. I had no idea about the first question. I only recognized Versace so picked that.
