计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (2): 55-62.doi: 10.11896/jsjkx.221200169

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于知识图谱与用户兴趣的推荐算法

许天月1, 柳先辉2, 赵卫东2   

  1. 1 同济大学电子与信息工程学院 上海201804
    2 同济大学电子与信息工程学院CAD研究中心 上海201804
  • 收稿日期:2022-12-29 修回日期:2023-05-24 出版日期:2024-02-15 发布日期:2024-02-22
  • 通讯作者: 柳先辉(xianhui_l@163.com)
  • 作者简介:(1468770549@qq.com)
  • 基金资助:
    国家重点研发计划(2022YFB3305700)

Knowledge Graph and User Interest Based Recommendation Algorithm

XU Tianyue1, LIU Xianhui2, ZHAO Weidong2   

  1. 1 College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
    2 CAD Research Center,College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
  • Received:2022-12-29 Revised:2023-05-24 Online:2024-02-15 Published:2024-02-22
  • About author:XU Tianyue,born in 1998,master.Her main research interests include know-ledge graph and recommender systems.LIU Xianhui,born in 1979,Ph.D,associate researcher(associate professor).His main research interests include machine learning,data mining and big data,and networked manufacturing.
  • Supported by:
    National Key Research and Development Program of China(2022YFB3305700).

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摘要： 为了解决协同过滤推荐算法中存在的冷启动以及数据稀疏性等问题,文中引入了具有丰富语义信息和路径信息的知识图谱。基于其结构特征,将图神经网络应用于知识图谱的推荐算法得到了研究者的青睐。推荐算法的核心在于获取物品特征和用户特征,然而,该方面研究的重点在于更好地表达物品特征,而忽略了用户特征的表示。文中在知识图谱图神经网络的基础上,提出了一种基于知识图谱与用户兴趣的推荐算法。该算法通过引入一个独立的用户兴趣捕获模块,来学习用户历史信息,引入了用户兴趣,使得推荐算法在用户和物品两个方面都得到了良好表征。实验结果表明,在MovieLens数据集上,基于知识图谱与用户兴趣的推荐算法实现了数据的充分利用,具有良好的效果,对推荐准确性起到了促进作用。

关键词: 推荐算法, 知识图谱, 图神经网络, 用户兴趣

Abstract: In order to solve the problems of cold start and data sparsity in the collaborative filtering recommendation algorithm,the knowledge graph with rich semantic information and path information is introduced in this paper.Based on its graph structure,the recommendation algorithm which applies graph neural network to knowledge graph is favored by researchers.The core of the recommendation algorithm is to obtain item features and user features,however,research in this area focuses on better expressing item features and ignoring the representation of user features.Based on the graph neural network,a recommendation algorithm based on knowledge graph and user interest is proposed.The algorithm constructs user interest by introducing an independent user interest capture module,learning user historical information and modeling user interest,so that it is well represented in both users and items.Experimental results show that on the MovieLens dataset,the recommendation algorithm based on knowledge graph and user interest realizes the full use of data,has good results and promotes the accuracy of recommendation.

Key words: Recommendation algorithm, Knowledge graph, Graph neural networks, User interest

中图分类号: 

  • TP391
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