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应用科学学报 ›› 2025, Vol. 43 ›› Issue (1): 123-136.doi: 10.3969/j.issn.0255-8297.2025.01.009

• 计算机应用专辑 • 上一篇    下一篇

基于多跳机制的扩散图谱推荐模型

刘珈宁1, 张思佳1,2,3, 张正龙1, 王祎涵1, 安宗诗1   

  1. 1. 大连海洋大学 信息工程学院, 辽宁 大连 116023;
    2. 大连海洋大学 设施渔业教育部重点试验室, 辽宁 大连 116023;
    3. 大连市智慧渔业重点实验室, 辽宁 大连 116023
  • 收稿日期:2024-07-17 出版日期:2025-01-30 发布日期:2025-01-24
  • 通信作者: 张思佳，副教授，研究方向为自然语言处理、知识图谱。E-mail:zhangsijia@dlou.edu.cn E-mail:zhangsijia@dlou.edu.cn
  • 基金资助:
    辽宁省教育厅高等学校基本科研项目面上项目（No.LJKMZ20221095）；辽宁省重点研发计划项目（No.2023JH26,No.10200015）资助

A Diffusion Map Recommendation Model Based on Multi-hop Mechanism

LIU Jianing1, ZHANG Sijia1,2,3, ZHANG Zhenglong1, WANG Yihan1, AN Zongshi1   

  1. 1. College of Information Engineering, Dalian Ocean University, Dalian 116023, Liaoning, China;
    2. Key Laboratory of Environment Controlled Aquaculture Ministry of Education, Dalian Ocean University, Dalian 116023, Liaoning, China;
    3. Dalian Key Laboratory of Smart Fisheries, Dalian 116023, Liaoning, China
  • Received:2024-07-17 Online:2025-01-30 Published:2025-01-24

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摘要： 针对基于知识图谱的推荐系统中存在的高阶建模困难与用户特征建模不足的问题，提出基于多跳机制的扩散图谱推荐模型（a diffusion map recommendation model based on multi-hop mechanism,MultiHop-GDN）。该模型通过端到端方法挖掘知识图谱高阶语义信息，涵盖知识图谱构建、特征提取网络构建与多跳扩散模型构建三部分内容。利用用户特征和项目特征构建知识图谱；深入分析用户兴趣、偏好和历史行为等信息，构建用户画像和兴趣模型；提出特征提取网络捕获深层次语义信息，通过本文模型的计算得到预测值。在两个公开数据集的对比实验表明，MultiHop-GDN能够同时实现用户和项目的高阶建模，与其他代表论文的模型相比有良好的推荐效果。

关键词: 知识图谱, 推荐系统, 多跳机制, 扩散模型, 深度学习

Abstract: To address the challenges of high-order modeling and insufficient user feature modeling in knowledge graph-based recommendation systems, a diffusion map recommendation model based on multi-hop mechanism (MultiHop-GDN) is proposed. This model mines high-order semantic information from the knowledge graph through an end-to-end method, covering three parts: knowledge graph construction, feature extraction network design and multi-hop diffusion model development. The knowledge graph is constructed using user and project attributes, enabling in-depth analysis of information such as user interests, preferences, and historical behaviors to build user portraits and interest models. A feature extraction network is introduced to capture deep semantic information and obtain prediction values through the calculation of this model. Comparative experiments on two public datasets show that MultiHop-GDN effectively achieves high-level modeling of both users and projects, outperforming other representative models in recommendation effects.

Key words: knowledge graph, recommender system, multi-hop mechanism, diffusion model, deep learning

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