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From Pavan K Narayanan <pavan.naraya...@gmail.com>
Subject Re: HELP for implicit data feed back - beginner
Date Sat, 23 Nov 2013 12:43:11 GMT
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
> >>
> >>
> >
>
>

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