How recommendation and music discovery works on Spotify? This time around, we'll dive deeper into the topic with a breakdown of Spotify's recommender system (which can be, to an extent, extrapolated to other DSP recommendation engines). A few weeks back, we kicked off the year with an article covering the ins and outs of the famed TikTok "For You" algorithm. The topic of unveiling AI-driven recommender systems and providing music professionals with the resources and tools they need to understand and manage these algorithms will be a big focus for Music Tomorrow throughout 2022. Music professionals rely on recommender systems across platforms like Spotify and YouTube to amplify the ad budgets, connect with the new audiences, and all-around execute successful release campaigns - while often having no clear vision of how these systems operate and how to leverage them to amplify artist discovery. Yet, as algorithmic recommendations take center stage in the music discovery landscape, the professional community at large still perceives these recommender algorithms as black boxes. On Spotify, for instance, over one third of all new artist discoveries happen through "Made for You" recommendation sessions according to the recently released Made to be Found report. Back in 2020, as much as 62% of consumers rated across platforms like Spotify and YouTube among their top sources of music discovery - and be sure that a healthy chunk of that discovery is going to be mediated by recommender systems. If you're curious to find out more about how artists and their team can influence the recommender system to optimize algorithmic traffic, check out our ongoing series on Recommender System Optimization.Īs we move ahead into the 2020s, an ever-increasing share of music consumption and discovery is going to be mediated by AI-driven recommendation systems. This article gets into great detail on how Spotify Recommender System works, outlining the process Spotify recommender follows to understand assets and users on the platform.
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