融合相似用户影响效应的知识图谱推荐模型  

A knowledge graph recommendation model incorporating the influence effect of similar users

作　　者：张若一 金柳 马慧芳 王亦可 李清风 ZHANG Ruo-yi;JIN Liu;MA Hui-fang;WANG Yi-ke;LI Qing-feng(College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070;China Transport Information Center Co.,Ltd.,Beijing 100088;Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]西北师范大学计算机科学与工程学院,甘肃兰州730070 [2]中国交通信息科技集团有限公司,北京100088 [3]桂林电子科技大学广西可信软件重点实验室,广西桂林541004

出　　处：《计算机工程与科学》2023年第3期520-527,共8页Computer Engineering & Science

基　　金：国家自然科学基金(61762078,61363058,61802404);甘肃省自然科学基金(20JR10RA076,21JR7RA114);甘肃省高校产业支撑项目(2022CYZC-11);西北师范大学研究生科研资助计划(2021KYZZ02103);西北师范大学青年教师能力提升计划(NWNU-LKQN2019-2);广西可信软件重点实验室研究课题(kx202003)。

摘　　要：知识图谱(KG)具有丰富的结构化信息,能有效缓解推荐模型的稀疏性和冷启动问题,提升推荐系统的准确性与可解释性。近年来,融合知识图谱的端到端推荐模型成为技术趋势。提出了一种融合相似用户影响效应的知识图谱推荐模型,该模型在有效利用知识图谱的前提下,扩充了用户与项目之间的交互方式。首先,利用图神经网络邻域聚合策略与注意力机制,分别捕获用户与项目在知识图谱上的2种高阶表示;其次,根据相似用户的影响效应,设计影响力增强层,捕获相似用户影响效应的潜在表示;最后,将上述3种表示共同反馈到多层感知机中,输出预测分值。在真实数据集上的实验结果验证了所提模型的有效性和效率。Knowledge Graph(KG)owns rich structured information,which can effectively alleviate the sparsity and cold start problem of recommendation model and improve the accuracy and interpretability of recommendation.In recent years,the end-to-end recommendation models incorporating knowledge graph have become a technological trend.This paper proposes a KG recommendation model incorporating the influence effects of similar users.The proposed method expands the interaction between users and items to effectively utilize the knowledge graph.Firstly,a graph neural network neighbor aggregation strategy and an attention mechanism are used to capture two higher-order representations of users and items on the knowledge graph respectively.Then,an influence enhancement layer is designed to capture potential representations of similar users’influence effects according to their influence effects.Finally,these three representations are fed together into a multi-layer perceptron to output prediction scores.Experimental results on real datasets show the effectiveness and efficiency of the proposed model.

关 键 词：知识图谱 推荐系统 注意力机制 影响效应 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]