南京大学学报（自然科学版）2023，Vol.59Issue(6)：937-946,10.DOI:10.13232/j.cnki.jnju.2023.06.004
基于知识图谱的轻量级图卷积网络推荐
Lightweight graph convolutional network recommendation based on knowledge graph
摘要
Abstract
Knowledge graph provides the recommendation system with rich and structured information to improve the recommendation accuracy.Recent trend in technology is to design end-to-end models based on propagation,but some propagation-based approaches are unable to capture the higher-order collaboration signals of a project.The most common forms included in general graph convolutional networks are feature transformation,nonlinear activation and neighborhood aggregation.However,experience has shown that feature transformation and nonlinear activation do not necessarily have a positive effect on collaborative filtering recommendations,even worse,they may reduce recommendation performance and make training more difficult.To solve these problems,a lightweight graph convolutional network recommendation model based on knowledge graph is proposed.Firstly,samples from the physical neighbors are taken as receptive fields,and entities in the knowledge graph are embedded and propagated through multiple iterations to obtain higher-order neighborhood information.Receptive fields are combined with neighborhood information and possible deviations to calculate the entity representation.Receptive fields can be extended to multi-hops to simulate higher-order connectivity and capture users'potential long-distance interests.Secondly,neighborhood aggregation is used to predict ratings between users and projects,which not only simplifies model design,but also improves model validity and accuracy.Finally,the proposed model is applied to three datasets of movie,book and music recommendations,and experimental results show that the proposed method outperforms other recommendation baselines.关键词
知识图谱/推荐/图卷积网络/协同过滤/嵌入传播Key words
knowledge graph/recommendation/graph convolutional network/collaborative filtering/embedded propagation分类
信息技术与安全科学引用本文复制引用
彭永梅,童向荣..基于知识图谱的轻量级图卷积网络推荐[J].南京大学学报（自然科学版）,2023,59(6):937-946,10.基金项目
国家自然科学基金(62072392,61972360),山东省重大科技创新工程(2019522Y020131),山东省自然科学基金(ZR2020QF113),烟台市重点实验室:高端海洋工程装备智能技术 （62072392,61972360）
