Assistant Professor Ashton Anderson discussed recommender systems with Spotify’s senior director of research at a recent event hosted by the Schwartz Reisman Institute for Technology and Society. (Photo: Ken Jones)
Have you ever clicked on a curated Spotify playlist and asked yourself, “How does it understand me so well?” There is a reason why the platform has 500 million monthly active users: their ability to curate content that magically fits each user’s taste is far from accidental, but the result of sophisticated systems built on complex machine learning.
With an impressive song library of over 82 million tracks, one might wonder how Spotify decides which songs to suggest. The answer lies in the platform’s recommender algorithms, which develop custom content relevant for a specific user based on their past behaviour. Recommender systems are so ubiquitous today that we have come to expect personalized content on nearly every digital platform that we interact with.
In a special event hosted by the Schwartz Reisman Institute for Technology and Society (SRI) at the University of Toronto’s Rotman School of Management, SRI Research Lead Ashton Anderson conversed with Mounia Lalmas, senior director of research at Spotify, who explored some of the behind-the-scenes techniques used by the world’s largest music streaming platform.
An assistant professor in U of T’s Department of Computer Science, Anderson’s research in the field of computational social science led him to Spotify in 2018 to explore how algorithms impact user journeys, where he collaborated with Lalmas, who leads a team of interdisciplinary researchers working on personalization. In addition to her work at Spotify, Lalmas holds an honorary professorship at University College London and is a distinguished research fellow at the University of Amsterdam.
At the outset of her presentation, Lalmas emphasized the importance of “creating personalized listening experiences.” She delved into how Spotify processes data to match users and tracks, providing insight into the types of data used, which includes not only the taste preferences of users, but also their interactions with the app, time of day, and history.
Rather than seeking to predict the next click, Lalmas emphasized the goal at Spotify is to develop algorithms that guide users’ long-term journeys, and that this necessarily shifts what factors determine a “good recommendation.” She rounded out her presentation by sharing recent methods her team has developed, including how the platform is supporting users to explore diverse content and helping to connect users with new artists.