Reinforcement Learning Techniques to Continuously Adapt and Optimize Recommender Systems Based on User Interaction

Abstract

Abstract—Reinforcement Learning (RL) has emerged as a
powerful approach in recommender systems, modeling user
interactions as sequential decision-making processes to deliver
adaptive, personalized, and context-aware recommendations.
Unlike traditional methods that focus on short-term accuracy,
RL emphasizes long-term user engagement by dynamically
responding to evolving behaviors and preferences. This paper
systematically reviews RL-based recommender frameworks,
including value-based, policy-based, actor–critic, and hybrid
approaches, as well as emerging trends such as explainable RL,
fairness-aware design, and privacy-preserving mechanisms.
Multi-dimensional evaluation metrics, including diversity,
novelty, and serendipity, are discussed, alongside integration
strategies combining RL with collaborative and content-based
filtering for enhanced scalability and robustness. Although
there has been significant progress, problems of data sparsity,
cold-start situations, computational issues, and interpretability
still exist. The review gathers existing research findings that
illuminate the limitations and identifies new research avenues
that can be used to develop user-friendly scalable and
transparent RL-based recommender systems in future
applications. The systems hold the promise of enhancing user
satisfaction and engagement greatly across the digital platforms,
creating a useful advantage to online retailing, streaming
services, and social media. Further ongoing innovation in RL
approaches is needed to satisfy the increasing requirements of
smart, flexible recommendation systems