Conversational Recommender System Using Deep Reinforcement Learning

摘要

Deep Reinforcement Learning (DRL) uses the best of both Reinforcement Learning and Deep Learning for solving problems which cannot be addressed by them individually. Deep Reinforcement Learning has been used widely for games, robotics etc. Limited work has been done for applying DRL for Conversational Recommender System (CRS). Hence, this tutorial covers the application of DRL for CRS. We give conceptual introduction to Reinforcement Learning and Deep Reinforcement Learning and cover Deep Q-Network, Dyna, REINFORCE and Actor Critic methods. We then cover various real life case studies with increasing complexity starting from CRS, deep CRS, adaptivity, topic guided CRS, deep and large-scale CRSs. We plan to share pre-read for Reinforcement Learning and Deep Reinforcement learning so that participants can grasp the material well.