Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (19): 98-106.DOI: 10.3778/j.issn.1002-8331.2201-0183

• Big Data and Cloud Computing • Previous Articles     Next Articles

Recommendation Algorithm Integrating Collaborative Knowledge Graph and Optimizing Graph Attention Network

TANG Hong, FAN Sen, TANG Fan   

  1. 1.School of Communication and Information Engineering, Chongqing University of Posts and Communications, Chongqing 400065, China
    2.Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Communications, Chongqing 400065, China
  • Online:2022-10-01 Published:2022-10-01



  1. 1.重庆邮电大学 通信与信息工程学院,重庆 400065 
    2.重庆邮电大学 移动通信技术重庆市重点实验室,重庆 400065

Abstract: Considering the problems of data sparseness and high model complexity in recommendation algorithms, this paper proposes a recommendation model that integrates collaborative knowledge graphs and optimized graph attention networks. Firstly, the knowledge graph and the user-item interaction graph are combined into a collaborative knowledge graph and embedded into the optimized graph attention network model, which cannot only alleviate the data sparsity problem well, but also mine potential interests and high order relationship of users. Secondly, using an optimized graph convolutional network, by removing feature transformation and nonlinear activation modules, the model complexity can be greatly reduced without affecting the overall recommendation performance. Combined with the deviation-based attention mechanism, the deviation between the candidate item and the user’s real interest item can be sensed in time to improve the training efficiency of the model. Finally, simulation experiments are carried out on the Movielens dataset and the Double dataset, and it is concluded that the recommended performance and time complexity of the algorithm are effectively improved compared with the comparison algorithm.

Key words: knowledge graph, graph convolutional network, attention mechanism, recommendation algorithm

摘要: 考虑到推荐算法存在数据稀疏及模型复杂度较高等问题,提出了一种融合协同知识图谱与优化图注意网络的推荐模型。将用户/项目知识图谱与用户-项目交互图结合为协同知识图谱,嵌入到优化的图注意网络模型中,这不仅可以很好地缓解数据稀疏问题,还能更大程度地挖掘用户的潜在兴趣和高阶关系;使用优化的图卷积网络,通过去除特征转换和非线性激活模块,可以在不影响整体推荐性能的基础上极大地降低模型复杂度;结合基于偏差的注意力机制,及时感知候选项目与用户真实感兴趣项目之间的偏差,提升模型的训练效率。在Movielens数据集和Douban数据集上进行仿真实验,结果表明该算法在推荐性能和时间复杂度方面,相比对比算法均得到了有效的提升。

关键词: 知识图谱, 图卷积网络, 注意力机制, 推荐算法