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From Antony Adopo <saius...@gmail.com>
Subject Re: HELP for implicit data feed back - beginner
Date Fri, 22 Nov 2013 21:37:40 GMT
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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