Reinforcement Learning in Recommender Systems - Some Challenges

Reinforcement Learning in Recommender Systems - Some Challenges

Explore the challenges of applying reinforcement learning to recommender systems in this 52-minute lecture by Craig Boutilier from Google and the University of Toronto. Delve into key issues such as scaling for multiple users and actions, handling stochastic action sets, and addressing user learning over long horizons. Examine the MDP/RL formulation for maximizing user engagement, and investigate item interactions on recommendation slates. Learn about user choice assumptions, Q-learning decomposition, and slate optimization techniques. Analyze synthetic experiments and the robustness of models to different user choice behaviors, including the cascade model.