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From Antony Adopo <saius...@gmail.com>
Subject Re: HELP for implicit data feed back - beginner
Date Mon, 25 Nov 2013 12:10:46 GMT
Hello, I disover one ebook and an article which help me about my problem:
the article :http://www.csulb.edu/web/journals/jecr/issues/20044/Paper1.pdf
the ebook :
http://www.amazon.fr/gp/product/B00BEQ82FY/ref=oh_d__o00_details_o00__i00?ie=UTF8&psc=1

very interesting


2013/11/23 Manuel Blechschmidt <Manuel.Blechschmidt@gmx.de>

> Hello Pavan,
> the following project is preconfigured using maven, m2eclipse and a normal
> eclipse project layout:
>
> https://github.com/ManuelB/facebook-recommender-demo
>
>
> https://raw.github.com/ManuelB/facebook-recommender-demo/master/docs/EclipseWorkspace.png
>
> When you execute the maven goal "mvn install" followed by "mvn
> embedded-glassfish:run" it will generate a war and deploy it on an embedded
> glassfish.
>
> If you have a lot of data you should build a model e.g. similarities or a
> matrix factorization on hadoop and then deploy this model in a live
> environment.
>
> Here is an excellent blog post by Sebastian:
>
> http://ssc.io/deploying-a-massively-scalable-recommender-system-with-apache-mahout/
>
> Hope that helps
>     Manuel
>
>
> On 23.11.2013, at 07:49, Sebastian Schelter wrote:
>
> > 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
> >>>>>
> >>>>>
> >>>>
> >>>
> >>>
> >>
> >
>
> --
> Manuel Blechschmidt
> M.Sc. IT Systems Engineering
> Dortustr. 57
> 14467 Potsdam
> Mobil: 0173/6322621
> Twitter: http://twitter.com/Manuel_B
>
>

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