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] >>
