With its huge music library and recent acquisition of music-data specialist The Echo Nest, Spotify was already a force to be reckoned with in the streaming music space. Now, an intern at Spotify has published a blog post explaining his work to step up the company’s game even more by incorporating deep learning models to power better song recommendations. The post author, a University of Ghent (in Belgium) Ph.D. student named Sander Dieleman, explains that the goal of his research was to make it easier for new or obscure songs to get included among listeners’ recommendations. Essentially, he wants to help listeners hear new songs by recommending others that sound like the songs they already like, instead of songs that other people with similar tastes also like. Dieleman’s project at Spotify expands on a paper he and fellow Ghent researchers published in December.