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.blechschm...@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.o...@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.blechschm...@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.sm...@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" <saius...@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.o...@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 > >
