E-TUP：融合E-CP与TUP的联合知识图谱学习推荐方法

doi:10.3778/j.issn.1002-8331.2211-0464

摘要/Abstract

摘要： 目前，大部分将知识图谱引入推荐系统的方法只是将已知的表层知识图谱实体进行引入，没有对图谱的内在关系进行预测和挖掘，因此无法利用知识图谱中的隐藏关系。针对上述问题，提出联合学习推荐模型E-TUP（enhance towards understanding of user preference），使用E-CP（enhance canonical polyadic）进行知识图谱补全并将完整信息进行传递。利用储存空间负采样方法，将优质负例三元组进行存储，并随训练过程进行更新，以提高知识图谱补全中负例三元组的质量。链接预测实验结果显示，储存空间方法使E-TUP模型链接预测准确率对比现有模型最高提升10.3%。在MovieLens-1m和DBbook2014数据集上进行推荐实验，在多个评价指标上取得最佳结果，对比现有模型实现最高5.5%的提升，表明E-TUP可以有效利用知识图谱中的隐藏关系提高模型推荐准确率。基于汽车维修数据进行推荐实验，结果表明E-TUP可以有效推荐相关知识。

关键词: 知识图谱, 推荐系统, 链接预测, 联合学习, 知识图谱补全

Abstract: At present, most of the methods to introduce knowledge graphs into recommendation systems only introduce known surface knowledge graph entities, without predicting and mining the intrinsic relationships of the graphs, and thus cannot exploit the hidden relationships in the knowledge graphs. In this paper, the joint learning recommendation model E-TUP (enhance towards understanding of user preference) is proposed to address the above problem, and E-CP (enhance canonical polyadic) is used to complement the knowledge graph and deliver the complete information. A storage space negative sampling method is used to store and update high-quality negative triples with the training process to improve the quality of negative triples in the knowledge graph complementation. Experimental results on link prediction show that the storage-space approach improves the link prediction accuracy of the E-TUP model by up to 10.3% compared to existing models. Recommendation experiments on the MovieLens-1m and DBbook2014 datasets achieve the best results on several evaluation metrics, achieving up to 5.5% improvement, indicating that E-TUP can effectively exploit the hidden relationships in the knowledge graph to improve recommendation accuracy. Finally, the results of the recommendation experiments based on automotive maintenance data show that E-TUP can effectively recommend relevant knowledge.

Key words: knowledge graph, recommendation system, link prediction, joint learning, knowledge graph complement

赵博, 王宇嘉, 倪骥. E-TUP：融合E-CP与TUP的联合知识图谱学习推荐方法[J]. 计算机工程与应用, 2024, 60(8): 99-109.

ZHAO Bo, WANG Yujia, NI Ji. E-TUP:Joint Knowledge Graph Learning Recommendation Method Incorporating E-CP and TUP[J]. Computer Engineering and Applications, 2024, 60(8): 99-109.

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