计算机工程与应用2024，Vol.60Issue(8)：99-109,11.DOI:10.3778/j.issn.1002-8331.2211-0464
E-TUP:融合E-CP与TUP的联合知识图谱学习推荐方法
E-TUP:Joint Knowledge Graph Learning Recommendation Method Incorporating E-CP and TUP
摘要
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.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Key words
knowledge graph/recommendation system/link prediction/joint learning/knowledge graph complement分类
信息技术与安全科学引用本文复制引用
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):99-109,11.基金项目
国家自然科学基金(61403249) （61403249）
科技创新2030—"新一代人工智能"重大项目(2020AAA0109300). （2020AAA0109300）
