Chapter 17 in MiA has a decent description of this method. On Wed, Jun 22, 2011 at 1:17 AM, Ted Dunning <[email protected]> wrote:
> 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] >> >> >> >> >
