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From Pat Ferrel <>
Subject Re: FPGrowth and Recommendations
Date Tue, 03 Mar 2015 01:14:09 GMT
Jeff, are you trying to build a general recommender? Or a shopping cart recommender? FP was
used to find things often bought together, which means recommendations based on some partial
group of items (watchlist, wishlist, shopping cart). FPG has been deprecated in favor of newer

There are references at the top of this page
that may help for a general recommender but things bought together would require a slightly
different approach.

Think of the shopping-cart-id as if it were a user-id. Create a cooccurrence matrix using
spark-itemsimilarity as described in the above references. The shopping cart ids will disappear
leaving an item-id followed by a list of similar items. Index this in a search engine and
use the current partial shopping cart as the query. You’ll get back an ordered list of the
items most commonly bought with items in the current shopping cart.

On Mar 2, 2015, at 3:37 PM, Andrew Musselman <> wrote:

Hi Jeff, as I recall the map-reduce-based fp-growth solution was
problematic, and it's been either deprecated or removed.

There are better solutions under the "recommendations" tab at

And I would encourage your updating your version of Mahout to 0.9 or to the
master branch at, since 0.7 from that blog
is outdated.


On Mon, Mar 2, 2015 at 3:26 PM, Jeff Isenhart <>

> Hi,
> New to mahout and fp growth. I havefollowed this example:
> I generated nice output informationlike this (as an example):
> [abc,def,ghi] => klm,confidence:0.597, support:0.01, lift: 57.415,
> conviction: 2.453…...
> Now I am not clear on how to model“recommendations” where given items
> [qrs, tuv] recommend wxy basedon confidence level. Am I to make lookups
> based on the above results or use one of the several recommender and
> similarity classesin mahout? A bit lost on where to start.
> Thanks

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