Earticle

초록

영어

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.

목차

Abstract
1. Introduction
2. Related work
2.1 Session-based Recommendation System
2.2 Large Language Model for Recommendation
2.3 Exploring Large Language Model for Recommendation.
3. Method
4. Results and Discussion
5. Conclusion
Acknowledgement
References

키워드

저자

• Jee Young Lee [ Associate Professor, Department of Software, SeoKyeong University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380