计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 220900180-7.doi: 10.11896/jsjkx.220900180

• 大数据&数据科学 • 上一篇    下一篇

基于知识图残差注意力网络的推荐方法

范洪玉, 张永库, 孟祥福   

  1. 辽宁工程技术大学电子与信息工程学院 辽宁 葫芦岛 125105
  • 发布日期:2023-11-09
  • 通讯作者: 孟祥福(marxi@126.com)
  • 作者简介:(fan_hy1997@163.com)
  • 基金资助:
    辽宁省教育厅科学研究项目(LJKZ0355)

Recommendation Method Based on Knowledge Graph Residual Attention Networks

FAN Hongyu, ZHANG Yongku, MENG Xiangfu   

  1. School of Electronic and Information Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China
  • Published:2023-11-09
  • About author:FAN Hongyu,born in 1997,postgra-duate.Her main research interests include recommendation system and so on.
    MENG Xiangfu,born in 1981,Ph.D,professor,is a senior member of China Compuer Federation.His main research interests include big data analysis and query,spatio-temporal data mining,and machine learning algorithms.
  • Supported by:
    Liaoning Provincial Department of Education Scientific Research Project(LJKZ0355).

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摘要： 随着互联网的高速发展,推荐系统已成为缓解信息过载的重要手段之一。当前的推荐方法主要采用深度学习模型挖掘用户对项目的兴趣度,但是目前使用图神经网络的推荐方法无法有效表征用户和项目之间的交互行为,并且网络层数的增加会产生梯度消失问题。因此,文中提出了一种新的模型,基于正交变换和图上下文的知识图嵌入方式并融合残差网络和注意力机制的模型。首先,通过嵌入节点的邻居属性来表征用户与项目的交互行为,然后通过图神经网络和残差网络分析用户项目交互行为,最后利用注意力机制区分不同的邻域,提高推荐的准确性。通过在Alibaba-fashion和Last-FM两个真实数据集上进行实验,结果表明所提方法能够显著提升推荐效果。

关键词: 推荐系统, 图神经网络, 知识图谱, 协同过滤, 嵌入传播

Abstract: With the rapid development of the Internet today,recommendation system has become an important means to relieve the information overload.Current recommendation methods mainly use deep learning model to mine users’ interests in the project.However,the current recommendation methods using graph neural networks cannot effectively represent the interaction behaviors between users and items well,and the increase in the number of network layers will cause the problem of gradient disappearance.Therefore,this paper proposes a model that combines the GC-OTE knowledge graph embedding approach with residual networks and attention mechanisms.First,the interaction information of users or items is represented by embedding the neighbor attributes of nodes,then user-item interactions are analyzed by graph neural and residual networks,and finally,attention mechanisms are used to distinguish different neighborhoods.Experiments on two real-world datasets Alibaba-fashion and Last-FM demonstrate that the proposed method can significantly improve the recommendation performance.

Key words: Recommendation system, Graph neural network, Knowledge graph, Collaborative filtering, Embedding propagation

中图分类号: 

  • TP311
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