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From Sebastian Schelter <ssc.o...@googlemail.com>
Subject Re: HELP for implicit data feed back - beginner
Date Fri, 22 Nov 2013 23:29:07 GMT
Antony,

You don't need numeric ratings or preferences for your recommender. I
would suggest you start by using

o.a.m.cf.taste.impl.recommender.GenericBooleanPrefItemBasedRecommender

which has explicitly been built to support scenarios without ratings. I
would further suggest to use

o.a.m.cf.taste.impl.similarity.LogLikelihoodSimilarity

as similarity measure.

Best,
Sebastian


On 22.11.2013 22:37, Antony Adopo wrote:
> ok, thank you so much. I will start like this and after do some tricks to
> increase accuracy
> 
> 
> 2013/11/22 Manuel Blechschmidt <Manuel.Blechschmidt@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.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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