  Deep reinforcement learning (DRL) has been proven its efficiency in capturing
users' dynamic interests in recent literature. However, training a DRL agent is
challenging, because of the sparse environment in recommender systems (RS), DRL
agents could spend times either exploring informative user-item interaction
trajectories or using existing trajectories for policy learning. It is also
known as the exploration and exploitation trade-off which affects the
recommendation performance significantly when the environment is sparse. It is
more challenging to balance the exploration and exploitation in DRL RS where RS
agent need to deeply explore the informative trajectories and exploit them
efficiently in the context of recommender systems. As a step to address this
issue, We design a novel intrinsically ,otivated reinforcement learning method
to increase the capability of exploring informative interaction trajectories in
the sparse environment, which are further enriched via a counterfactual
augmentation strategy for more efficient exploitation. The extensive
experiments on six offline datasets and three online simulation platforms
demonstrate the superiority of our model to a set of existing state-of-the-art
methods.
