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Journal of Graphics ›› 2025, Vol. 46 ›› Issue (5): 1050-1060.DOI: 10.11996/JG.j.2095-302X.2025051050

• Computer Graphics and Virtual Reality • Previous Articles     Next Articles

Knowledge-aware recommendation based on hypergraph representation learning and Transformer model optimization

ZUO Yuqi1(), ZHANG Yunfeng1(), ZHANG Qiuyue2, XU Yingcheng3   

  1. 1 School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan Shandong 250014, China
    2 School of Information Science and Engineering, Shandong Normal University, Jinan Shandong 250014, China
    3 School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan Shandong 250014, China
  • Received:2024-08-22 Accepted:2024-12-25 Online:2025-10-30 Published:2025-09-10
  • Contact: ZHANG Yunfeng
  • About author:First author contact:

    ZUO Yuqi (1998-), master student. Her main research interest covers recommendation algorithm. E-mail:yqzuo@mail.sdufe.edu.cn

  • Supported by:
    Natural Science Foundation of Shandong Province(ZR2022MF245);Foundation items:Youth Talent Introduction and Cultivation Plan in Colleges and Universities of Shandong Province;Shandong Province Natural Science Fundation Youth Branch(ZR2023QF161);Shandong Provincial Youth Fund(ZR2024QF158);Youth Innovation Team in Colleges and universities of Shandong Province(2022KJ185)

Abstract:

The recommendation algorithm based on knowledge graphs has emerged as a significant research focus and hotspot in the field of recommender systems in recent years. The introduction of knowledge graphs, enables the acquisition of auxiliary information about items, thereby significantly enhancing the capabilities of recommendation systems and providing users with more precise and personalized recommendation experiences. In response to this trend, a knowledge-aware recommendation model optimized via hypergraph representation learning and the Transformer model was proposed. This model leveraged the unique advantages of hypergraphs in handling high-order relationships to directly model the complex interaction information between users and items, thereby greatly enriching their interaction information. Since local graphs lack global interaction information between users and items, global hypergraphs were constructed within local graphs. On the other hand, nonlocal graphs contain redundant information, so nonlocal hypergraphs were built to capture more comprehensive interaction information between users and items. Additionally, the attention mechanism of the Transformer model was employed to strengthen the collaboration between user nodes and item nodes, mining more valuable preference information from noisy user interaction data such as clicks on uninteresting items. This optimized the embeddings of user and item nodes, mitigating the impact of noise and enhancing the recommendation performance based on user preferences.

Key words: knowledge graph, knowledge-aware recommendation, hypergraph, Transformer, attention mechanism

CLC Number: