tldr - powered by Generative AI

• Spotify is building an encoder system that can encode a user's state into embeddings that are sensitive to actions and changes in satisfaction.
• They have built a simulator that can simulate user reactions to certain content, which is used to train the recommendation algorithm.
• A/B testing is used to compare agents trained on good simulators versus less good simulators.
• Spotify is transitioning to a more farsighted approach to content recommendation, optimizing for long-term fulfilling content diet rather than clicks or streams.
• They are investing in data instrumentation to understand how users interact with their content and to create reusable data sets.
• Spotify has shared machine learning models that provide information on user affinities, similarities, and clustering, which are useful for many different features.
• Machine learning models are created for specific use cases, such as Discover Weekly or search, and optimize for different goals.