By analyzing the acoustic properties of songs on Spotify, intern and PhD student Sander Dieleman hopes to advance the streaming service’s recommendation algorithms to aid users in discovering new and lesser known music. Rather than basing recommendations on the choices people with similar tastes make, they would be based on songs the user listens to. This method, which requires deep learning, would then mix more obscure but user relevant songs into the recommendations. Continue reading Spotify Intern Creates System to Improve Recommendations