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
