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From Antony Adopo <saius...@gmail.com>
Subject Re: HELP for implicit data feed back - beginner
Date Fri, 22 Nov 2013 21:22:48 GMT
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.Smith@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" <saius1er@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.open@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
> >> >
> >>
> >>
>
>

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