performed by Group 3

Recommending interesting music to people is at the core of modern music players. Based on existing ratings and similarity between users, recommended songs have a high chance of being to the taste of a user. Your task is to apply a state-of-the-art graph-based recommendation algorithm to a dataset from Yahoo! Music.

• Run the Alternating Least Squares (ALS) algorithm
• Convert latent vector into actual recommendations
• Determine the quality of prediction by comparing the computed predictions with the test set
• Stretch goals: Map ids back to actual songs, build interface where one can select their favorite songs and gets recommendations for others

This analysis will be using the Okapi Collaborative Filtering library on top of Giraph.

The music interest data is available in text format. The data will be available on HDFS at /user/hannesm/lsde/music . Example:

Music interest data (user, song, rating)

49      2169    5
49      2180    2
50      311     5
50      325     1

• Large-Scale Parallel Collaborative Filtering for the Netflix Prize
• Pregel: a system for large-scale graph processing