On Tue, Jan 26, 2010 at 5:04 PM, Sean Owen <sro...@gmail.com> wrote: > You're saying content-based recommendation, in practice, is often a > matter of substituting one dominant item attribute in place of items > -- recommending on artist, rather than artist track. OK, check, one > can do that in the current framework by using artists as items. So I > think that's supported for free. >
I think so as well. > > And maybe my other notion of a way to bring content-based > recommendation into the framework -- some organized framework for > constructing and tuning a notion of item similarity based on > attributes -- also has merit and belongs in the category of > "content-based" techniques. > I didn't mention that there is quite a bit of scope here for decomposition based algorithms. There is no reason at all for all the attributes of an item to not contribute to the "meaning" of that item. The problem there really comes from the fact that attributes cohere in two ways. One way is by cooccurring on a single item. That is definitely semantically important and has implications for recommendation performance because it helps us understand items themselves in a better and less sparse way. Another way is by cooccurring within the set of preferences for a single user. That is also important since it indicates that something about those attributes is important relative to user preferences. 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). Anyway, the great and glorious advantage of decompositional techniques here is that they will embed items in a semantic space based on all available information. That provides a very natural way to integrate all attributes for recommendation. -- Ted Dunning, CTO DeepDyve
