Yes, nice, I follow. >From a CF perspective, item-level cooccurrence results in the same thing as user-level cooccurrence of attributes. If I know what items users like and how much, and know how much items resemble each other based on attributes, I am basically predicting what the user will like based on item attributes -- user X will item A because it shares attributes with item B, and X likes B, and so we can infer X likes B's attributes... etc.
As you can see, I'm eager to fit this into the canonical CF framework without cheating the meaning of "content-based" recommender. It's not (just) laziness, but, would certainly be tidy to fit this idea into the existing framework meaningfully rather than bolt on another paradigm. I guess I feel one role of a framework like Mahout is to tease out and capture the similarity and order in these diverse ideas, rather than just implement each one by one. On Wed, Jan 27, 2010 at 1:22 AM, Ted Dunning <ted.dunn...@gmail.com> wrote: > Most decomposition algorithms have trouble when presented with more than one > kind of cooccurrence such as this presents. My guess is that you would get > most of the available mileage by ignoring item level cooccurrence and > focusing on user level attribute cooccurrence. This makes decomposition > easy and presumably gives you the best of all worlds since item cooccurrence > is a special case of user cooccurrence. Decomposition approaches are nice > as well since they would use artist when it helps and ignore it when it > doesn't (to use the music case again).
