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
