At Spotify we have have over 60 million active users who have access to a vast music catalog of over 30 million songs. Our users have a choice to follow thousands of artists and hundreds of their friends and create their own music graph. On our service they also discover new and existing content by experiencing a variety of music promotions (album releases, artist promos), which get served over our ad platform. These options have empowered our users and made them really engaged. Over time they have created over 1.5 billion playlists and just last year they streamed over 7 billion hours worth of music.
But at times an abundance of options has also made our users feel a bit lost. How do you find that right playlist for your workout from over a billion playlists? How do you discover new albums which are relevant to your taste? We help our users discover and experience relevant content by personalizing their experience on our platform.
Personalizing user experience involves learning their tastes and distastes in different contexts. A metal genre listener might not enjoy an announcement for a metal genre album when they are trying to put their kid to sleep and playing kid’s music at night. Serving them a recommendation for a kid’s music album might be more relevant in that context. But this experience might not be relevant for another metal genre listener who doesn’t mind receiving metal genre album recommendations during any context. These two users with similar listening habits might have different preferences. Personalizing their experiences on Spotify according to their respective taste in different contexts helps us make them more engaged.
Given these product insights we set out to build a personalization system which could analyze both real-time and historic data to understand user’s context and behavior respectively. Over time we’ve evolved our personalization tech stack due to a flexible architecture and ensured we used the right tools to solve the problem at scale.