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From "Nick Pentreath" <nick.pentre...@gmail.com>
Subject Re: HELP for implicit data feed back - beginner
Date Fri, 22 Nov 2013 21:33:28 GMT
If you want to try collaborative filtering you can start with a "preference " value of 1 for
each user, item pair ("order" in this case) and see how that works. Then go from there and
you can try to tweak things.




With that data size you should be able to do it in memory on a reasonably


large size single machine so probably don't need a distributed approach just yet

—
Sent from Mailbox for iPhone

On Fri, Nov 22, 2013 at 11:23 PM, Antony Adopo <saius1er@gmail.com> 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.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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