青岛大学学报（自然科学版）2024，Vol.37Issue(2)：41-46,54,7.DOI:10.3969/j.issn.1006-1037.2024.02.08
基于关系缩放模型的电商知识图谱链接预测问题研究
Research on the E-commerce Knowledge Graph Link Prediction Problem Based on the Relation Scale Model
摘要
Abstract
To address the issues of low accuracy in link prediction models for e-commerce knowledge graphs and the repeated recommendation of the same type of products,an improved Relation Scale(RS)model was proposed.The strength of relationships between the head and tail entities of triples was assessed using the TransE and TuckER model.The weights of all relationship paths were determined by introducing a scaling factor,thus enhancing the model's convergence speed.Experimental results show that the MRR,Hits@1,Hits@3 and Hits@10 of the improved models are all enhanced based on the OpenBG500 dataset.The MRR and Hits@10 for the RSTransE model increase by 47.4%and 71.1%respectively,compared with the traditional TransE model.The MRR and Hits@10 for the RSTuckER model increase by 35.8%and 28.4%respectively,compared with the traditional TuckER model.These findings indicate that the RS model can more accurately predict user needs and achieve more personalized and precise recommenda-tion results.关键词
推荐系统/关系缩放/知识图谱/链接预测Key words
referral system/relational scale/knowledge graph/link prediction分类
信息技术与安全科学引用本文复制引用
潘亚男,王军..基于关系缩放模型的电商知识图谱链接预测问题研究[J].青岛大学学报（自然科学版）,2024,37(2):41-46,54,7.基金项目
山东省自然科学基金(批准号:ZR2020MG012)资助. （批准号:ZR2020MG012）
