  Reinforcement learning (RL) has shown great promise in optimizing long-term
user interest in recommender systems. However, existing RL-based recommendation
methods need a large number of interactions for each user to learn a robust
recommendation policy. The challenge becomes more critical when recommending to
new users who have a limited number of interactions. To that end, in this
paper, we address the cold-start challenge in the RL-based recommender systems
by proposing a meta-level model-based reinforcement learning approach for fast
user adaptation. In our approach, we learn to infer each user's preference with
a user context variable that enables recommendation systems to better adapt to
new users with few interactions. To improve adaptation efficiency, we learn to
recover the user policy and reward from only a few interactions via an inverse
reinforcement learning method to assist a meta-level recommendation agent.
Moreover, we model the interaction relationship between the user model and
recommendation agent from an information-theoretic perspective. Empirical
results show the effectiveness of the proposed method when adapting to new
users with only a single interaction sequence. We further provide a theoretical
analysis of the recommendation performance bound.
