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From Manuel Blechschmidt <Manuel.Blechschm...@gmx.de>
Subject Re: HELP for implicit data feed back - beginner
Date Fri, 22 Nov 2013 21:35:05 GMT
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.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
>>>>> 
>>>> 
>>>> 
>> 
>> 

-- 
Manuel Blechschmidt
M.Sc. IT Systems Engineering
Dortustr. 57
14467 Potsdam
Mobil: 0173/6322621
Twitter: http://twitter.com/Manuel_B


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