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From Tolga Oral <>
Subject Re: Recommending items for anonymous users
Date Tue, 20 Apr 2010 21:15:34 GMT
Unfortunately my user-user similarity information is quite limited too, it
probably covers only 50% of the user base.

I was actually just reading TreeClusteringRecommender and
TreeClusteringRecommender2, I was thinking to get a small number of clusters
then pick top items from each cluster to
create some diversity.

I might do a A/B testing with popularity versus above method to see if there
is any significant difference for the user.

On Tue, Apr 20, 2010 at 4:58 PM, Sean Owen <> wrote:

> Ah well, if you have a priori user-user similarity, you can do
> user-based recommendation for your new user even with no user-item
> links for him/her. As long as you know user-user similarities you're
> OK.
> Ted's suggestion is essentially a variant of this. You could use
> TreeClusteringRecommender to do what he says.
> Your second question is a bit different from a question of
> recommendation. Perhaps you would base such a list on *recent*
> popularity? or recent positive change in popularity? You could
> populate it with things that used to be popular?
> On Tue, Apr 20, 2010 at 9:25 PM, Tolga Oral <> wrote:
> > PlusAnonymousUserDataModel will work once the user clicks on couple items
> on
> > the site, however still doesnt solve the dead-start problem. We are
> creating
> > user similarities based on different attributes and use the similarities
> to
> > recommend items (doesn't solve all cases though)
> >
> > However I am still interested in figuring out the most popular items with
> > some diversity (otherwise new "interesting/good" items have no chance of
> > ever getting in recommendations) ? Any ideas how we can do this in
> mahout?
> >

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