Building a course recommendation engine for Wesleyan students


Members
Anastasios Germanidis, Brian Macharia, Lingyuan Ke



We consider the problem of processing K-Nearest Neighbor (KNN) queries over large data sets where the index is jointly maintained by a set of machines in a computing cluster. The proposed RankReduce approach uses locality sensitive hashing (LSH) together with a MapReduce implementation, which by design is a perfect match as the hashing principle of LSH can be smoothly integrated in the mapping phase of MapReduce.

Pybrain 
git clone git://github.com/pybrain/pybrain.git


http://www.lsdsir.org/wp-content/uploads/2010/05/lsdsir10-2.pdf

http://people.csail.mit.edu/indyk/p117-andoni.pdf