ok, thank you so much. I will start like this and after do some tricks to increase accuracy
2013/11/22 Manuel Blechschmidt <manuel.blechschm...@gmx.de> > Hallo Antony, > you can use the following project as a starting point: > https://github.com/ManuelB/facebook-recommender-demo > > Further you can purchase support for mahout at many companies e.g. MapR, > Apaxo or Cloudera. > > For implicit feedback just use a 1 as preference and the > LogLikelihoodSimilarity. > > Hope that helps > Manuel > > On 22.11.2013, at 16:22, Antony Adopo wrote: > > > thanks. > > I've already seen this but my question is Mahout propose some > collaborative > > filtering function not based on preference? or how modelize these with > > purchases? > > > > Thanks > > > > > > 2013/11/22 Smith, Dan <dan.sm...@disney.com> > > > >> Hi Anthony, > >> > >> I would suggest looking into the collaborative filtering functions. It > >> will work best if you have your customers segmented into similar groups > >> such as those that buy high end goods vs low end. > >> > >> _Dan > >> > >> On 11/22/13 11:04 AM, "Antony Adopo" <saius...@gmail.com> wrote: > >> > >>> 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 > >>>>> > >>>> > >>>> > >> > >> > > -- > Manuel Blechschmidt > M.Sc. IT Systems Engineering > Dortustr. 57 > 14467 Potsdam > Mobil: 0173/6322621 > Twitter: http://twitter.com/Manuel_B > >
