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From Chris Schilling <>
Subject Re: Which is more effective?
Date Tue, 21 Jun 2011 23:10:44 GMT
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
> 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 <> 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

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