Use ItemSimilarityJob instead of RowSimilarityJob, its the easy-to-use wrapper around that :)
On 11.04.2013 19:28, Sean Owen 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
