  Reinforcement learning (RL) in recommendation systems offers the potential to
optimize recommendations for long-term user engagement. However, the
environment often involves large state and action spaces, which makes it hard
to efficiently learn and explore. In this work, we propose a sample-efficient
representation learning algorithm, using the standard slate recommendation
setup, to treat this as an online RL problem with low-rank Markov decision
processes (MDPs). We also construct the recommender simulation environment with
the proposed setup and sampling method.
