The Exploit-Explore Dilemma In Music Recommendation

Were The Rolling Stones right when they said, "You can't always get what you want; but ifyou try sometime you get what you need"? Recommendation systems are the crystal ball of the Internet: predicting user intentions, making sense of big data, and delivering what people are looking for before they even know they want it.Pandora radio is best known for the Music Genome Project; the most unique and richly labeled music catalog of 1.5 million+ tracks. While this content-based approach to music recommendation is extremely effective and still used today as the foundation to the leading online radio service, Pandora has also collected more than a decade of contextual listener feedback in the form of more than 65 billion thumbs from 79M+ monthly active users who have created more than 10 billion stations. This session will look at how the interdisciplinary team at Pandora goes about making sense of these massive data sets to successfully make large scale music recommendations to our listeners.As opposed to more traditional recommender systems which need only to recommend a single item or set of items, Pandora's recommenders must provide an evolving set of sequential items, which constantly keep the experience new and exciting. The talk will present an overview of recommendation at Pandora, followed by a deep dive into the challenges of recommending content in the long tail. A mixture of experts based system will be discussed as well as the many data resources required to bring small stations to life for both new and indie artists.Furthermore, in this talk I will present a dynamic ensemble learning system that combines musicological data and machine learning models to provide a truly personalized experience. This approach allows us to switch from a lean back experience (exploitation) to a more exploration mode to discover new music tailored specifically to users individual tastes.Following this session the audience will have an in-depth understanding of how Pandora uses science to determine the perfect balance of familiarity, discovery, repetition and relevance for each individual listener, measures and evaluates user satisfaction, and how our online and offline architecture stack plays a critical role in our success.