Zhenwen Dai

Reinforcement Learning at Spotify: An Example with Interactive Radio

At Spotify, we are developing reinforcement learning based personalization experience. In this talk, I will present an example of our reinforcement learning development: a Radio playlist experience powered by reinforcement learning, which is called Interactive Radio. In an Interactive Radio listening session, a reinforcement learning agent refreshes the radio station upon user feedback. This is different from the typical ranking paradigms in recommender system literature. In this talk, I will show the simulation-based approach that we developed to tackle this problem and discuss the results of our approach from online tests.

Zhenwen Dai is a staff research scientist at Spotify. Zhenwen’s research interest is to develop machine learning systems that can automatically learn from large amounts of unlabeled data and make informed decisions. Prior to this, he was a machine learning scientist at Amazon in Cambridge, UK. He did his PhD in machine learning at Goethe University Frankfurt and was followed by a postdoc at Sheffield University.

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