Hi Antony, In my experience, using such content-based features tends to make the recommendations worse. But of course, this can be different in your case.
I suggest you start with a basic item-based recommender that ignores user descriptions. In your production system, you should create the functionality to run A/B tests, so that you can test different recommenders and evaluate them according to some business metric. If you have this machinery set up, you can easily test more complicated recommenders (such as one that leverages user descriptions) and see if they peform better than standard ones. --sebastian On 23.11.2013 01:09, Antony Adopo wrote: > Ok, thanks. > But does exist a combining scenario including user description > (job,category) and (customerid,itemid) to better accurate recommendation) . > For example, in case where I used User_Based recommender system? > > > 2013/11/23 Sebastian Schelter <ssc.o...@googlemail.com> > >> 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 >>>> >>>> >>> >> >> >
