These are the slides of the talk I gave at the O’Reilly AI conference in London. My first external talk about work at Spotify on personalisation for Spotify home. The focus is how to identify what success means so that the machine learning algorithm (in this case based on multi-armed contextual bandits) captures that users differ into how they listen to music and playlist consumption varies a lot. We show that by basing the reward functions on thresholds based on the distribution of streaming time boosts performance when using counterfactual evaluation methodologies. This is is just the beginning and there will be more to come on this and other research we are doing at Spotify.
Hi, very interesting slides, would it also be possible to post the updated version that were presented in the criteo meetup during sigir in July 2019?