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=UTF8psc=1
very interesting
2013/11/23 Manuel
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
@mahout.apache.org
Objet : Re: HELP for implicit data feed back - beginner
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
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?
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
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
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
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
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
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
If you want to try collaborative filtering you can start with a preference
value of 1 for each user, item pair (order in this case) and see how that
works. Then go from there and you can try to tweak things.
With that data size you should be able to do it in memory on a reasonably
large
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
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
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
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