Earticle

초록

영어

Traditional recommendation systems face limitations associated with data scarcity and structural complexity. In contrast, graph-based methods can effectively leverage extensive relational information. In this study, we examine the core principles of dynamic clustering and LightGCN to enhance the recommendation performance of artificial intelligence (AI) agents. Based on our findings, we propose an embedding propagation and aggregation strategy incorporating dynamic clustering to extend and refine the LightGCN architecture. Experimental results demonstrate significant performance improvements compared with the conventional LightGCN across key evaluation metrics, confirming the effectiveness of the proposed method for next-generation, AI-agent-based recommendation services.

목차

Abstract
1. Introduction
2. GNN-based Recommendation Models
3. Improvement of LightGCN utilizing Dynamic Clustering
3.1 Recommendation Algorithm Based on BPRMF and NGCF
3.2 LightGCN-based Recommendation Algorithm
3.3 Recommendation Algorithm of LightGCN Based on Dynamic Clustering
4. Experimental Results and Considerations
4.1 Experimental Results for GCN, NGCF, LightGCN, and LightGCN-DC (100k, 1M) Models
4.2 Experimental Results of GCN NGCF, LightGCN, and LightGCN-DC 실험 결과 (10M, 25M)
5. Conclusion
References

키워드

저자

• Chang Woo Lee [ Ph.D. Student, AI Convergence, aSSIST University, Korea ]
• Joongho Chang [ Professor, AI Convergence, aSSIST University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380