machine learning

The Proposal

This student proposal has two parts. The first part is to enhance performance for the recommender modules via Apache Mahout integration.

The Recommender API module and its helper modules was developed as a GSoC 2009 project. Those modules enable Drupal sites to provide content recommendation services based on users browsing history, Fivestar ratings, product purchasing history, and so on, similar to what http://amazon.com offers. However, I received many feedback from users complaining the performance/scalability issue of those modules: for sites with >1k nodes or users, the modules won't work (using 4GB RAM, running for >10 hours). This is simply not acceptable, because it is those sites with lots of nodes/users who need the recommendation service the most.

The performance issue is due to the following reasons:

About me: I'm a PhD student at the University of Michigan School of Information, with a research focus on designing recommender systems. I'm also a Drupal fan, and have been using Drupal and developing for Drupal for 3 years. Last year I participated in GSoC 2009 and developed the recommender bundle modules. For more information about me, please visit http://michiza.com

Overview: The proposed module will make content recommendations using Google Analytics tracking data, and strive for a balance between "exploitation" (recommending popular contents) and "exploration" (recommending new and trendy contents) using the "multi-armed bandit" (MAB) algorithm

Overview: The Recommender Bundle provides a set of modules that generate recommendations and personalized views in a wide range of areas. For example, "Customers who bought this also bought" for Ubercart, Facebook-like new friends suggestions for social network sites, Youtube-like related videos for media sites, or the classical example of generating personalized node recommendations based on users access history, and much more.