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
