Ever wonder how you can train algorithms to introduce fresh and new information while still making the recommendations targeted and personalized to the user? This talk will cover how and when to leverage reinforcement learning techniques for personalized recommendations problems in fields from everything from e-commerce to click-through-rate optimization, and how these methods can solve common challenges encountered in recommender systems such as the cold start problem and the echo chamber effect.
Speakers
Claire Longo
Machine Learning Engineering Manager, Opendoor
Claire is an Engineering Manager at Opendoor leading the MLOps team who is focused on building infrastructure to support and scale our ML models in production. Claire has worked as a data scientist and machine learning engineer and is passionate about solving challenges with ML models in production, ethical AI research, and recommender systems.
