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Comment: Re:Just tried this out (Score 1) 174

by lucubrationowl (#30002122) Attached to: Going Head To Head With Genius On Playlists

The first track that played was a System of a Down tune. Which is about as pop as it gets. The ones after didn't get much better. If they really want to use this to push less played songs which have potential, they should actually better get some.

There's a way lesser-know bands can upload their songs into their database for free, this leads to lesser-known songs being tagged with adjectives, this leads to a music search engine that you can use to find those lesser-known songs you like.

Comment: Re:Mathematics != human preference (Score 2, Informative) 174

by lucubrationowl (#30002036) Attached to: Going Head To Head With Genius On Playlists

Stop being arithmetic supergeeks wanting to put everything inside a box, and start figuring out how to get all these weird unpredictable people to input valuable data into your system.

Google figured this out more than a decade ago, so why are we still seeing stupid mathematical and "pattern-based" algorithms every year?

The neural network is trained on crowd-sourced data. TRANSLATION: These supergeeks actually DID figure out how to get all these weird unpredictable people to input valuable data into their system. The solution they designed is that people will play their Facebook game,, and the statistically significant answers to the quiz game are tagged to the song clips. These tagged song clips are then used as a training data set for the neural network. The machine algorithm is a result of the collective intelligence of all the players of


Going Head To Head With Genius On Playlists 174

Posted by timothy
from the put-that-man-down dept.
brownerthanu writes "Engineers at the University of California, San Diego are developing a system to include an ignored sector of music, dubbed the 'long tail,' in music recommendations. It's well known that radio suffers from a popularity bias, where the most popular songs receive an inordinate amount of exposure. In Apple's music recommender system, iTunes' Genius, this bias is magnified. An underground artist will never be recommended in a playlist due to insufficient data. It's an artifact of the popular collaborative filtering recommender algorithm, which Genius is based on. In order to establish a more holistic model of the music world, Luke Barrington and researchers at the Computer Audition Laboratory have created a machine learning system which classifies songs in an automated, Pandora-like, fashion. Instead of using humans to explicitly categorize individual songs, they capture the wisdom of the crowds via a Facebook game, Herd It, and use the data to train statistical models. The machine can then 'listen to,' describe and recommend any song, popular or not. As more people play the game, the machines get smarter. Their experiments show that automatic recommendations work at least as well as Genius for recommending undiscovered music."

The meat is rotten, but the booze is holding out. Computer translation of "The spirit is willing, but the flesh is weak."