联合学习用户端和项目端知识图谱的个性化推荐  

Combining User-end and Item-end Knowledge Graph Learning for Personalized Recommendation

作　　者：梁浩宏 古天龙[2] 宾辰忠[2] 常亮 LIANG Hao-hong;GU Tian-long;BIN Chen-zhong;CHANG Liang(School of Computer Science and Information Security,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China;Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)

机构地区：[1]桂林电子科技大学计算机与信息安全学院,广西桂林541004 [2]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：《计算机科学》2021年第5期109-116,共8页Computer Science

基　　金：国家自然科学基金项目(62066010,61862016,61966009);广西自然科学基金项目(2020GXNSFAA159055);广西创新驱动重大专项项目(AA17202024)。

摘　　要：如何在已有的用户行为和辅助信息的基础上准确建模用户的偏好非常重要。在各种辅助信息中,知识图谱(Know-ledge Graph,KG)作为一种新型辅助信息,其节点和边包含了丰富的结构信息和语义信息,近年来受到了越来越多研究者的关注。大量研究表明,在个性化推荐中引入知识图谱可以有效地提高推荐的性能,并增强推荐的合理性和可解释性。然而,现有的方法要么是在KG上探索每个用户-项目交互对(user-item)的独立子路径,要么使用图表示学习的方法在KG中分别学习目标用户(user)或项目(item)的表示,虽然都取得了一定的效果,但是前者没有充分捕获用户-项目(user-item)在KG上的结构信息,后者在产生嵌入(embedding)表示的过程中忽略了user和item的相互影响。为了弥补上述方法的不足,提出了一种联合学习用户端和项目端知识图谱(User-end and Item-end Knowledge Graph,UIKG)的新模型。该模型通过挖掘用户和项目在各自KG中的关联属性信息,并通过联合学习有效地捕获用户的个性化偏好与项目之间的关联性。具体的操作步骤是,利用基于图卷积神经网络的方法从用户知识图谱中学习用户表示向量,再将用户表示向量引入项目知识图谱中联合学习得到项目表示向量,实现用户端KG和项目端KG的无缝统一,最后通过多层感知器进行偏好预测,得到用户对项目的偏好概率,从而更有效地挖掘KG中的高阶结构信息和语义信息来捕获用户的个性化偏好。在公开数据集上的实验结果表明,与基线方法相比,UIKG在Recall@K指标上提高了2.5%~13.6%,在AUC和F1指标上提高了0.4%~5.8%。How to accurately model user preferences based on existing user behavior and auxiliary information is of great important.Among all kinds of auxiliary information,Knowledge Graph(KG)as a new type of auxiliary information,its nodes and edges contain rich structural information and semantic information,and attracts a growing researchers’attention in recent years.Plenty of studies show that the introduction of KG in personalized recommendation can effectively improve the performance of recommendation,and enhance the rationality and interpretability of recommendation.However,the existing methods either explore the independent meta-paths for user-item pairs over KG,or adopt graph representation learning on whole KG to obtain representations for users and items separately.Although both have achieved certain effects,the former fails to fully capture the structural information of user-item pairs in KG,while the latter ignores the mutual effect between target user and item during the embedding propagation.In order to make up for the shortcomings of the above methods,this paper proposes a new model named User-end and Item-end Knowledge Graph(UIKG),which can effectively capture the correlation between users’personalized preferences and items by mining the associated attribute information in their respective KG.Specifically,we learn the user representation vectors from the user KG,and then introduce the user representation vectors into the item KG based on the method of graph convolution neural network to jointly learn the item representation vectors,so as to realize the seamless unity of the user KG and the item KG.Finally,we predict the user preference probability of the item through MLP.Experimental results on open datasets show that,compared with the baseline method,UIKG improves by 2.5%~13.6%on Recall@K index,and 0.4%~5.8%on AUC and F1 indexes.

关 键 词：个性化推荐 知识图谱 图卷积神经网络 联合学习 

分 类 号：TP331[自动化与计算机技术—计算机系统结构]