Music Personalization at Spotify
Spotify is the world's largest on demand music streaming company, with over 75 million active listeners choosing what to listen to among tens of millions songs. Discovery and personalization is a key part of the experience and critical to the success of the creator and consumer ecosystem. In this talk, we'll discuss the state of our current discovery approaches, such as the Discover Weekly playlist that has already streamed billions of new discoveries and Fresh Finds, a scalable platform for brand new music that focuses suggestions on the long end of the popularity tail. We'll discuss the technologies at scale necessary to distill the information about music from our listeners and the world at large we collect outside of Spotify with the massive amounts of user item activity data we collect every day to create highly personalized music experiences. Entire teams at Spotify focus on understanding both the creator and listener through collaborative filtering, machine learning, DSP and NLP approaches we crawl the web for artist information, scan each note in every one of our millions of songs for acoustic signals, and model users' taste through a cluster analysis and in a latent space based on their historical and real time listening patterns. The data generated by these analyses have ensured our discovery products are precise and help our users enjoy music and media across our entire catalog. We'll dive deep into the workings of Discover Weekly, our marquee personalized playlist which updates weekly and reached 1 billion streams within the first 10 weeks from its release. The technology behind Discover Weekly is powered by a scalable factor analysis of Spotify's over two billion user generated playlists matched to each user's current listening behavior. We'll discuss its innovative genesis and the challenges and opportunities the system faces a year after its launch. We'll also discuss Spotify's home page, seen by each of our users, currently undergoing vast efforts around personalization to ensure each listener gets a targeted list of playlists, shows and music to select throughout their day. We'll discuss the various similarity metrics, ranking approaches and user modeling we're working on to increase precision and optimize for our users' happiness.
RecSys