Rethinking Reinforcement Learning for Recommendation

Modern recommender systems aim to improve user experience. As reinforcement learning (RL) naturally fits this objective---maximizing an user's reward per session---it has become an emerging topic in recommender systems. Developing RL-based recommendation methods, however, is not trivial due to the offline training challenge. Specifically, the keystone of traditional RL is to train an agent with large amounts of online exploration making lots of 'errors' in the process. In the recommendation setting, though, we cannot afford the price of making 'errors' online. As a result, the agent needs to be trained through offline historical implicit feedback, collected under different recommendation policies; traditional RL algorithms may lead to sub-optimal policies under these offline training settings.