Electrical engineers go head to head with Genius on music playlists

Electrical engineers recently pitted Genius – the music recommendation system in Apple's iTunes – against two experimental music recommender systems. Genius appears to capture acoustic similarities among songs within the same playlist, the researchers found. The University of California, San Diego electrical engineers also discovered that the music recommender they built from scratch can generate song playlists that human subjects thought were as good as those that Genius generates. The UC San Diego system works for songs that Genius knows nothing about.

UC San Diego electrical engineering Ph.D. student Luke Barrington presented these findings on October 28 at the 2009 International Society for Music Information Retrieval Conference (ISMIR 2009) in Kobe, Japan. (Read the paper at: http://cosmal.ucsd.edu/cal/pubs/Barrington-Genius-ISMIR09.pdf

"Our goal is to make a music recommendation tool that is as good as or better than Genius, but that does not require massive amounts of user data. The system we are developing can analyze and recommend completely unknown songs by new bands as accurately as it analyzes the most popular hits," said Barrington, who used the same underlying technology to create a series of music discovery games for Facebook (http://herdit.org) and a new kind of music search engine that will be available for beta testing next week (http://herdit.org/music/index.html). Watch a three minute video about the new music search engine at: http://cse-ece-ucsd.blogspot.com/2009/10/new-music-search-engine-on-way.html

Tools for creating automated music playlists are increasingly useful now that huge numbers of songs are available for download and streaming to anyone with an Internet connection. iTunes – the most popular music retailer on the planet – has sold more than 6 billion tracks. Genius uses "collaborative filtering" on these purchase statistics to help people organize their music and discover new songs they might like based on similarity to a "seed" song that they do like.

By averaging statistics about how millions of listeners purchase and play music, Genius appears to actually capture acoustic similarities between songs, according to the new research, which involved human evaluation of music recommendation systems and was led by researchers at the UC San Diego Jacobs School of Engineering. Because Genius is a proprietary system whose secrets are not available to the public, the researchers studied it by testing its song recommendations against comparable song suggestions from experimental music recommender systems that they fully understood.

via Electrical engineers go head to head with Genius on music playlists.