Ok. thanks for answering very quickly I forgot that to mention in the customer table there is a "job" variable and implicitly, I thought taht this variable will be also need for accurate recommendations. anyway
I have around 200 000 customers My order table is around 12 000 000 orders and I have around 2 000 000 distincts (customerid,itemid) tuples About (customerID,itemID) tuples, when I read Mahout or recommender system litterature, they use (customerID,itemID,*preference*) and I don't have *preference.* So exist an Mahout method or class that handle only (customerID,itemID) data? And it is possible to use external data as job or (RFM ) analysis to get something more accurate? Sorry (it's about 2 weeks, I have headache how organize all of this to build a great system). Propose your solutions and after, we'll see about 2013/11/22 Sebastian Schelter <ssc.o...@googlemail.com> > Hi Antony, > > I would start with a simple approach: extract all customerID,itemID > tuples from the orders table and use them as your input data. How many > of those do you have? The datasize will dictate whether you need to > employ a distributed approach to recommendation mining or not. > > --sebastian > > On 22.11.2013 19:21, Antony Adopo wrote: > > Morning, > > > > My name is Antony and I have a great recommender system to build > > > > I'm totally new on recommender systems. After reading all scientific > files, > > I didn't find relevant information to build mine. > > > > ok, my problem: > > > > I have to build a recommender systems for a retail industry which sold > > Building products > > > > I don't have Explicit data (ratings) > > > > I have only data about purchases and all transactions and order and > dates. > > as > > > > Orders table > > > > CustomerID > > Sales_ID > > Item_ID > > Dates > > Amount > > quantity > > channel_type (phone, mail,etc.) > > > > > > I have also specific informations about users > > > > Users table > > CustomerID > > Group (engaged, frequent,buyer, newyer, etc.) > > > > ... and product > > > > Item_ID > > Item_name > > Iteem_parent (hierarchy) > > > > I don't know how to use all these informations with mahout (or others > tools > > or method) to do a good recommendation system (all presents are based on > > ratings and all mahout systems I have seen are also based on ratings or > > preference) > > > > At beginning, I thought that I have to use classical datamining methods > as > > Clustering or association rules but accurately recommanding n products > > between 2000 products clustering in about 300 hierachical parents(not > > linked to domain) become difficult with classical data mining > > It is the reason that I turn myself to recommender system > > > > > > please Help > > thanks > > > >
