I think that with a slight bit of creative rewriting of results you can probably do some pretty fancy content based work with the current software.
Take the music example again. Take items as artists *or* albums *or* tracks. Explode the track listening history of a user into a mixed list of artists, albums and tracks. Recommend to users as usual to get a mixed list of different kinds of items. You might stop there and just display a heterogeneous list of things, but you could also slide through the list and replace artists with a popularity ranked list of their tracks, albums with something similar and then reduce duplicates, boosting items that get multiple credit. If you claim that the duplicate reduction is part of the presentation layer, then Taste as it stands can probably do fairly involved content based recommendations. On Sat, Jan 30, 2010 at 8:59 AM, Sean Owen <sro...@gmail.com> wrote: > 1) if your items are really dominated by one attribute (e.g. > recommending songs based on artist) then by thinking of that attribute > as the 'item' and applying regular CF, you're doing content-based > recommendation > 2) if you want to base item-item similarity on attributes and pair > that with item-based CF, you're doing content-based recommendation > -- Ted Dunning, CTO DeepDyve
