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From Tim Smith <timsmit...@hotmail.com>
Subject RE: Item recommendation w/o users or preferences
Date Sat, 11 Jan 2014 03:39:38 GMT
Yes, thank you - read through it and several of the item and user recommendation examples.
 The objective is to recommend based on the current basket - given no users/preferences (but
I do have a history of transactions) - I have been able to leverage the item mining algorithm
to calculate support and confidence values.  When I use a support threshold of 10% and group
by product and sort descending on confidence I am left we a ranking of item combos.  Basically
a top N list by item that I would use to drive the recommendations.  In the actual use case,
the requirement is not to recommend a product every time, rather the most likely products
based on a given basket - with my arbitrary thresholds, I would expect to exclude some baskets.


> From: NiMartin@pssd.com
> To: user@mahout.apache.org
> Subject: RE: Item recommendation w/o users or preferences
> Date: Sat, 11 Jan 2014 03:08:30 +0000
> 
> I think the key question is what is the desired outcome? If you don't have users (customers)
for which you'd like to generate recommendations that really handcuffs you from a recommendation
standpoint.
> 
> I'd recommend starting with a read through this: http://mahout.apache.org/users/recommender/recommender-first-timer-faq.html
to get a feel for what Mahout does in the recommendation space. 
> 
> -----Original Message-----
> From: Tim Smith [mailto:timsmith_s@hotmail.com] 
> Sent: Friday, January 10, 2014 8:27 PM
> To: user@mahout.apache.org
> Subject: Item recommendation w/o users or preferences
> 
> Say I have a retail organization that doesn't sell a diverse set of products, eg 2000,
but has many small transactions.  Also say that I don't have any user or preference information.
 Is it reasonable to use pattern mining (market baskets) and recommend items based on a set
of thresholds for support, confidence, and lift?  If not, what are my options?
>  		 	   		  
 		 	   		  
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