Reinforcement Learning-Based Personalized Recommendation via Collaborative LLM Reranking and User Preference Modeling
Keywords:
recommender systems, reinforcement learning, large language models, reranking, user preference modeling, fairness, governance, socio-technical systemsAbstract
Personalized recommendation has evolved from static preference estimation toward dynamic, context-sensitive decision systems that mediate attention, commerce, information access, and cultural exposure. This paper examines a system-level architecture for reinforcement learning-based personalized recommendation that combines sequential user preference modeling with collaborative large language model reranking. The central argument is that reinforcement learning offers a principled way to optimize long-horizon engagement and satisfaction, while large language models provide flexible semantic reasoning and collaborative reranking capacity that conventional recommender pipelines often lack. However, integrating these components creates substantial architectural, operational, and governance challenges. The paper analyzes retrieval, ranking, reranking, and policy layers as interdependent infrastructure rather than isolated algorithmic modules. It considers trade-offs involving latency, computational cost, explainability, fairness, robustness, privacy, and sustainability. It also discusses how collaborative reranking can improve relevance and diversity but may amplify feedback loops, popularity bias, and opaque optimization. The analysis emphasizes that deployment success depends less on any single model than on orchestration, monitoring, fallback mechanisms, human oversight, and policy-aware evaluation. Cross-domain examples from media, e-commerce, education, and public information systems illustrate how recommendation infrastructures shape user autonomy and institutional accountability. The paper concludes that reinforcement learning with collaborative language model reranking can produce adaptive and semantically rich recommendation, but only if designed as a governed socio-technical system with explicit constraints, auditable decision pathways, and continuous evaluation of fairness, robustness, and long-term societal effects.
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