计算机技术与发展2024，Vol.34Issue(9)：124-130,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0166
融合知识图谱和兴趣偏好的数字文化资源推荐方法
Digital Cultural Resource Recommendation Method Integrating Knowledge Graph and Interest Preferences
摘要
Abstract
In digital cultural resource recommendation,the precise matching between resources and user interests plays a key role.Although knowledge graphs effectively address the data sparsity and cold start problems in traditional recommendation algorithms,the static structure of knowledge graphs limits the understanding of the dynamic evolution of user interests.To address these issues,we propose a digital cultural resource recommendation method that integrates knowledge graphs and interest preferences(Knowledge Graph Interest Preferences,KGIP).Firstly,this method establishes the association between users and resources by constructing embedding repre-sentations of knowledge graphs.Secondly,it utilizes a long short-term memory network module to characterize user interests and explores complex features in users'long and short-term historical behaviors to more accurately capture user interest preferences.Finally,to fully utilize interest preferences and the association information between resources,the two feature representations are merged and fed into a multi-layer perceptron to learn the nonlinear structural features among different latent factors,introducing the Sigmoid activation function to obtain the final prediction results.Through multiple experiments on the Douban platform and the National Cultural Cloud platform dataset,the results show that KGIP performs well in digital cultural resource recommendation.关键词
推荐算法/知识图谱/长短期记忆网络/长短期兴趣偏好/数字文化资源推荐Key words
recommendation algorithm/knowledge graph/long short-term memory/long short-term interest preferences/digital cultural resource recommendation分类
信息技术与安全科学引用本文复制引用
张大更,王西汉,高全力..融合知识图谱和兴趣偏好的数字文化资源推荐方法[J].计算机技术与发展,2024,34(9):124-130,7.基金项目
国家自然科学基金项目(61902300) （61902300）
