Earticle

Large language models (LLMs) have emerged as powerful tools in the field of natural language processing (NLP) and have recently attracted considerable attention in the field of recommendation systems (RSs). In this regard, we investigated a method to simultaneously improve the accuracy of real-time recommendations and user satisfaction by combining LLMs and session-based recommendation systems. We propose the LReLLM4SBR model, which combines lightweight LLMs and reflective reinforcement learning to improve the performance of session-based recommendation systems. Through experiments on MovieLens and Amazon review datasets, LReLLM4SBR showed improved performance compared to existing models in Precision@K, Recall@K, MAP@K, and NDCG@K indices. This study suggests that combining lightweight LLM-based models and reinforcement learning techniques can improve the performance of session-based recommendation systems, and suggests the possibility of contributing to improving real-time personalized services of recommendation systems.