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From Nagu <thog...@gmail.com>
Subject Item-based Collaborative Filtering
Date Sun, 24 May 2009 23:48:51 GMT
Hi,

I need some guidance in implementing item-based Collaborative Filtering in
Mahout.

To give an example, I built a recommendation engine using python (and I
don't know damn about programming in general) last year based on some real
customer data from my company (e.g. customers who bought this stuff also
bought these...). I have some SQL procedures that spits the data for the CF
algorithm, and the python program crunches the dataset, and spits out the
recommendations for each customer (up to 50 recommended items) and it saves
recommendations in a database. I created a simple web framework using django
to present the recommendations given a customer ID. So sales teams can go to
an intranet page and get recommendations for any given customers. I update
the whole recommendations output every 15 days.

I want to produce something like this using Mahout just to get a feel of
Mahout. It will be something like, take this customer purchase history, and
run the item-based CF algorithm, give me the recommendations for a given
customer and save it in a database for me.

Where can I find some step by step implementation of some examples in
Mahout. I want to understand how this whole thing works and I want to start
tinkering with some real time data from the company where I work. I also
want to build some abstraction into this machine learning so that I can use
the output that comes out of Mahout and feed to internal business/customer
process and apply some business logic on top of this to make the results
more meaningful.

I am not sure if I am asking for too much, but I think I definitely need
some guidance.

Thank you,
Nagu

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