You can actually create a "user" #6 for your new order. Or you can use the "anonymous user" function of the library, although it's hacky.
We may be mixing up terms here. "DataModel" is a class that has nothing to do with Hadoop. Hadoop in turn has no part in real-time anything, like recommending to a brand-new "user". However it could build an offline model of item-item similarities and you could do something like a most-similar-items computation for a given new basket of goods. That is effectively what the "anonymous user" function is doing anyway. You can precompute all recommendations for all items but that's a lot of work! It's easy to get away with it with a thousand items, but with a million this may be infeasibly slow. On Thu, Apr 11, 2013 at 10:38 PM, Billy <b...@ntlworld.com> wrote: > As in the example data 'intro.csv' in the MIA it has users 1-5 so if I ask > for recommendations for user 1 then this works but if I ask for > recommendations for user 6 (a new user yet to be added to the data model) > then I get no recommendations ... so if I substitute users for orders then > again I will get no recommendations ... which I sort of understand so do I > need to inject my 'new' active order; along with its attached item/s into > the data model first and then ask for the recommendations for the order by > offering up the new orderId? or is there a way of merely offering up an > 'item' and then getting recommendations based merely on the item using the > data already stored and the relationships with my item? > > My assumptions: > #1 > I am assuming the data model is a static island of data that has been > processed (flattened) overnight (most probably by an Hadoop process) due to > the size of this data ... rather than a living document that is updated as > soon as new data is available. > #2 > I'm also assuming that instead of reading in the data model and > providing recommendations 'on the fly' I will have to run thru every item > in my catalogue and find out the top 5 recommended items that are ordered > with each item (most probably via a Hadoop process) and then store this > output in dynamoDb or luncene for quick access. > > Sorry for all the questions but it such an interesting subject. > > > On 11 April 2013 22:04, Ted Dunning <ted.dunn...@gmail.com> wrote: > >> Actually, making this user based is a really good thing because you get >> recommendations from one session to the next. These may be much more >> valuable for cross-sell than things in the same order. >> >> >> On Thu, Apr 11, 2013 at 12:50 PM, Sean Owen <sro...@gmail.com> wrote: >> >>> You can try treating your orders as the 'users'. Then just compute >>> item-item similarities per usual. >>> >>> On Thu, Apr 11, 2013 at 7:59 PM, Billy <b...@ntlworld.com> wrote: >>> > Thanks for replying, >>> > >>> > >>> > I don't have users, well I do :-) but in this case it should not >>> influence >>> > the recommendations >>> > >>> > , >>> > these need to be based on the relationship between >>> > " >>> > items ordered with other items >>> > in the 'same order' >>> > ". >>> > >>> > E.g. If item #1 has been order with item #4 >>> > >>> > [ >>> > 22 >>> > ] >>> > times and item #1 has been order with item #9 >>> > [ >>> > 57 >>> > ] >>> > times then >>> > if I added item #1 to my order >>> > these would both be recommended >>> > but item #9 would be recommended above item #4 purely based on the fact >>> that >>> > the relationship between item #1 and item #9 is greater than the >>> > relationship with item #4. >>> > >>> > What I don't want is; if a user ordered items #A, #B, #C separately >>> > 'at some point in their order history' then recommen >>> > d #A and #C to other users who order #B ... I still don't want this if >>> the >>> > items are similar and/or the users similar. >>> > >>> > Cheers >>> > >>> > Billy >>> > >>> > >>> > >>> > On 11 Apr 2013 18:28, "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 >>> >> >>
