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] >
