This project is a Hadoop/Mapreduce implementation of the K-nearest neighbor similarity algorithm. Our main contribution was parallelizing KNN's training method for use in MapReduce.

• Proposal
• Final paper

• args4j 2.0.21
• Hadoop 0.20.2

• smalltest.txt - A small database to test with. Can be run through the entire process.
• chunkit.py - Python script to chunk up a database file to be consumed by MapReduce.

k is the number of similarities per song to generate. r is the minimum number of ratings a similarity should have to be valid.

Sequential neighborhood generator:

KDD-Music-Recommender.jar -k N -r N database

MapReduce neighborhood generator:

hadoop jar KDD-Music-Recommender.jar -p [-k N] dirContainingChunks output

Query the neighborhood file:

KDD-Music-Recommender.jar -q -t D -n neighborhoodFile -u activeUserFile database