Toward Simulating Environments in Reinforcement Learning Based Recommendations

Understand

With the recent advances in Reinforcement Learning (RL), there have been tremendous interests in employing RL for recommender systems.

• However, directly training and evaluating a new RL-based recommendation algorithm needs to collect users' real-time feedback in the real system, which is time and efforts consuming and could negatively impact on users' experiences.
• Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms.
• Simulating users' behaviors in a dynamic system faces immense challenges -- (i) the underlining item distribution is complex, and (ii) historical logs for each user are limited.