You are right that sounds crazy. What I did was to model the target variable click trying to predict it with user features, item features and user x item interaction features.
On Wed, Jun 22, 2011 at 1:10 AM, Chris Schilling <[email protected]> wrote: > Hey Ted, > > I was wondering if you could briefly describe how one would make content > based recommendations using the SGD classifiers. > > Say I have item1: feature1a, feature1b, feature1c > and item2: feature2b, feature2c > > So, are you training a classifier for n labels, where n is the number of > items? That seems crazy cause you only have one feature vector per item. > > > On Jun 21, 2011, at 3:49 PM, Ted Dunning wrote: > > > I have used the SGD classifiers for content based recommendation. It > works > > out reasonably but the interaction variables can get kind of expensive. > > > > Doing it again, I think I would use latent factor log linear models to do > > the interaction features. See > > http://cseweb.ucsd.edu/~akmenon/LFL-ICDM10.pdf > > > > We have a half done implementation in Mahout. There was a student at > UCSD > > looking into completing it, but we don't have real results yet. > > > > On Wed, Jun 22, 2011 at 12:34 AM, 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] > >> > >
