Or you may want to look at recording purchases by user ID. Then use the 
standard recommender to train on (userID, itemsID, boolean). Then query the 
trained recommender thus: recommender.mostSimilarItems(long itemID, int 
howMany) This does what you want but uses more data than just what items were 
purchased together, sound like a shopping-cart recommender.

On Apr 11, 2013, at 10:28 AM, Sean Owen <sro...@gmail.com> wrote:

This sounds like just a most-similar-items problem. That's good news
because that's simpler. The only question is how you want to compute
item-item similarities. That could be based on user-item interactions.
If you're on Hadoop, try the RowSimilarityJob (where you will need
rows to be items, columns the users).

On Thu, Apr 11, 2013 at 6:11 PM, Billy <b...@ntlworld.com> wrote:
> I am very new to Mahout and currently just ready up to chapter 5 of 'MIA'
> but after reading about the various User centric and Item centric
> recommenders they all seem to still need a userId so still unsure if Mahout
> can help with a fairly common recommendation.
> 
> My requirement is to produce 'n' item recommendations based on a chosen
> item.
> 
> E.g. "if I've added item #1 to my order then based on all the
> other items; in all the other orders for this site, what are the
> likely items that I may also want add to my order based; on the item to
> item relationship in the history of orders of this site?"
> 
> Most probably using the most popular relationship between the item I have
> chosen and all the items in all the other orders.
> 
> My data is not 'user' specific; and I don't think it should be, but more
> like order specific as its the pattern of items in each order that should
> determine the recommendation.
> 
> I have no preference values so merely boolean preferences will be used.
> 
> If Mahout can perform these calculations then how must I present the data?
> 
> Will I need to shape the data in some way to feed into Mahout (currently
> versed in using Hadoop via Aws Emr using Java)
> 
> Thanks for the advice in advance,
> 
> Billy

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