A research team at Cornell University has developed a recommendation algorithm that could improve audio streaming and media services provider Spotify by incorporating both likes and dislikes.
The group showed that a listener is about 20% more likely to like a song if the algorithm recommending it is trained on 400,000 likes and dislikes, as opposed to an algorithm trained solely on likes.
Cornell's Sasha Stoikov said an algorithm that only keys on likes has a greater chance of recommending songs the listener dislikes.
The new Piki system picks music from a database of approximately 5 million songs and gives users $1 for every 25 songs they rate, which Stoikov said "incentivizes the user to vote truthfully."
From Cornell Chronicle
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