How to combine content based recommendation with CF

[Sören Brunk <so...@deri.org>]

Hi,

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?

Thanks,
Sören

Re: How to combine content based recommendation with CF

[Sean Owen <sr...@gmail.com>]

Yeah, it's not as simple as just averaging the output of two
similarity functions, as those values may not be at all comparable.
That would be nice. If the two similarity values have a
well-understood meaning you may be able to make a meaningful
combination of the two. I think this is hard to do correctly. People
on this list may have better thoughts about situations in which it
might be valid to combine similarity functions into a new one.

Yes, one solution is to zoom way out and not try to combine two
approaches at a low level but at a high level. For example what if you
recommend from two recommenders, and only keep the items that appear
in both outputs? That's pretty defensible.

On Wed, Oct 26, 2011 at 4:31 PM, Sören Brunk <so...@deri.org> wrote:
> Hi,
>
> 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?
>
> Thanks,
> Sören
>
>
>