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Going Head To Head With Genius On Playlists
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."
So, not really so much at all...?
The wisdom of the crowds seem to be proving you wrong.
It's exactly algorithms like the one used by Pandora that make me agree with the viewpoint that it's not possibly to calculate what "other music" I like based upon the "known music" that I like.
Anyone with a preference for Electro Pop will likely have been wondering when the hell Pandora would learn the difference between Miss Kittin and Scooter after mindlessly clicking "Dislike" on eurodance tracks when Pandora fails to see the difference between one type of electronic music with a repetitive beat and another.
The only really worthful algorithm we'll ever manage to produce is one that uses the collective intelligence of all its users.
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?
Setting aside the obvious joke, the "wisdom of crowds" has actually been proven to be useful in certain situations.
If you ask, say, a single person how many jelly beans are in a jar, he may or may not come close. If you ask several hundred people how many are in a given jar and then average their responses, the result tends to be surprisingly accurate.
The problem is that this is limited to situations requiring little to no topic-specific knowledge. Asking a large crowd of random people what the GDP of China is will be a waste of time. It's a technique that requires you to be asking the right questions.