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From Sören Brunk <>
Subject How to combine content based recommendation with CF
Date Wed, 26 Oct 2011 15:31:27 GMT

I'm trying to combine content based recommendation using metadata like 
tags with the collaborative filtering mahout offers. As suggested in the 
Mahout book, I'm creating a custom item similarity based on some 
attributes to inject content based recommendation into the framework.

But I'm not sure what's the best way to combine both approaches within 
one recommender. My idea was to create several similarities based on 
content, e.g. TagSimilarity, CategorySimilarity etc. (or one similarity 
by vectorizing all attribute information into one big vector).
For CF I use something LogLikelihoodSimilarity as the data contains only 
boolean preferences.
Then I would create some kind of combined similarity that takes the 
output of the individual similarities and combines them, possibly weighted.

As an alternative I thought of creating individual recommenders for the 
content based and the CF approach and then combine the results of both 
recommenders somehow.

Any suggestions what would the best approach here? Or would you do it in 
a completely different way?


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