Actually, I should mention that I have done user-feature recommendations and then (mis) used text retrieval to pull back items that have features as text. This works reasonably well and is pretty easy to do. You will have to watch out for very common features.
On Wed, Jun 22, 2011 at 12:50 AM, Sean Owen <[email protected]> wrote: > For #1 -- there is still an unanswered issue in there, and that's how you > extract features from items. I assume you already have some scheme for > that. > (The framework can't quite help you there.) > > But then the question remains how you compute similarity from features. So > #1 isn't a concrete possibility by itself. > > I am not sure if a classifier can be used very directly to figure a notion > of item-item similarity. I am sure it can be used in some sense, but it > doesn't seem like the most direct tool. A simpler notion, of similarity or > distance, is what you want, and that's a piece of clustering algorithms > really. > > On Tue, Jun 21, 2011 at 11:34 PM, Marko Ciric <[email protected]> > wrote: > > > Hi guys, > > > > When trying to do a content-based recommender, there could be two > > approaches > > with Apache Mahout: > > > > - Having a custom implemented Taste ItemSimilarity that is calculated > > with item features. > > - Classifying a data set with Mahout by representing items with > vectors. > > > > Has anybody had the experience with comparing performance/accuracy of > > those? > > > > Thanks > > > > -- > > Marko Ćirić > > [email protected] > > >
