mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Sebastian Schelter <ssc.o...@googlemail.com>
Subject Re: HELP for implicit data feed back - beginner
Date Sat, 23 Nov 2013 12:49:59 GMT
You can use it in a standard Java program, no need for JavaEE. There is
no special perspective for Mahout in Eclipse.

The easiest way to setup up a project is to configure a maven project
and use mahout-core as dependency.


On 23.11.2013 13:43, Pavan K Narayanan wrote:
> Hi Sebastian
> 
> Pardon my ignorance but how do you suggest we use this o.a.m.cf.taste.impl.
> recommender.GenericBooleanPrefItemBasedRecommender? Can we use it by coding
> in Java? - if yes, do we need Java EE? Is there a Mahout perspective for
> Eclipse IDE? Is it possible to use these in Mahout CLI? There are mentions
> of java programs in MiA but I am unsure how to setup Mahout in Java .
>  Please can you clarify this part .
> 
> Sincerely,
> Pavan
> 
> 
> 
> 
> On 23 November 2013 04:59, Sebastian Schelter <ssc.open@googlemail.com>wrote:
> 
>> 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
>>>>
>>>>
>>>
>>
>>
> 


Mime
View raw message