I am actually modeling charges. The main use case is when we have charges a,b
and c then we have charge d with a confidence, support, lift, etc...Then when I
get an account with some charges, I look this up against the model to see if
any charge may be missing (based on confidence....)+
I was under the impression FP was the best way to approach this, you mentioned
that a general recommender like this might require a different approach - any
resources/examples you can site?
On Monday, March 2, 2015 5:14 PM, Pat Ferrel <[email protected]>
wrote:
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 methods.
There are references at the top of this page
http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html 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 <[email protected]> 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
http://mahout.apache.org
And I would encourage your updating your version of Mahout to 0.9 or to the
master branch at https://github.com/apache/mahout, since 0.7 from that blog
is outdated.
Best
Andrew
On Mon, Mar 2, 2015 at 3:26 PM, Jeff Isenhart <[email protected]>
wrote:
> Hi,
> New to mahout and fp growth. I havefollowed this example:
> https://chimpler.wordpress.com/2013/05/02/finding-association-rules-with-mahout-frequent-pattern-mining/
> 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
>
