Allegedly, four million songs on Spotify have never been played once, thus it is understandable to feel overwhelmed by the perspective of having instant access to music catalogue virtually infinite. Searching for music in a streaming service is not like crate digging at record shops: the physical nature of the format needs to be replaced by new incentives that help the user discover his or her next favourite artist.
Recommendation algorithms are getting increasingly more sophisticated and individualisation can achieve an unexpected level of accuracy, leaving behind restrictive tags and obvious relationships between genres and artists.
Spotify presents in its system two functionalities aimed to discover music and understand our tastes better. Discover Weekly is a personalised playlist generated by an algorithm that compares the listening habits of the users with similar tastes to make high-precision recommendations, even unearthing the most hidden gems from the depths of Spotify. For its part, Spotify Taste Profiles is a collection of the music we listen to classified by genres and subgenres.
Matthew Ogle and Ajay Kalia, in charge of Discover Weekly and Spotify Taste Profiles, respectively, will give a keynote on the latest innovations in recommendation algorithms.
Matthew Ogle. His work takes place where music meets technology. Ogle has worked for Last FM and The Echo Nest, has programmed viral hits like Drinkify, and he is the creator of This Is My Jam, a music discovery service based on the favourite song of a user community. His mission in Spotify is to make easier for users to find their new favourite music via Discover Weekly.
Ajay Kalia. As Product Owner for Spotify’s Taste Profile initiative, Ajay Kalia oversees initiatives to identify each music fan’s unique taste and deliver personalized music experiences. Ajay has worked in technology product development for about a decade. Most recently, Ajay worked at music intelligence startup The Echo Nest, where he launched the Music Audience Understanding line that enables music services to predict a listener’s taste, personality, and interests based on music preference. Ajay authors the blog Skynet & Ebert, where he applies data analytics to crucial matters of pop culture. His work there has been covered by outlets including The Huffington Post, The Verge, USA Today, and the Sound Opinions radio show on NPR.