基于兴趣信息深度融合的网络图书资源推荐  

Network book resource recommendation based on deep fusion of interest information

作　　者：易晓宇 易绵竹[1] YI Xiao-yu;YI Mian-zhu(School of International Relations,National University of Defense Technology,Nanjing 210039,China;Book and Archive Information Center,Henan Normal University,Xinxiang 453000,China)

机构地区：[1]国防科技大学国际关系学院,南京210039 [2]河南师范大学图书与档案信息中心,河南新乡453000

出　　处：《吉林大学学报(工学版)》2024年第12期3614-3619,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：国防科技创新特区项目(18H86301ZT00500501);国家社会科学基金项目(14CYY051,18BYY235)。

摘　　要：为有效地为用户推荐图书资源,提出了一种基于兴趣信息深度融合的网络图书资源推荐方法。首先,采用K均值聚类算法对网络用户展开聚类处理,通过优化聚类中心提高用户聚类精度;其次,计算用户对图书资源的兴趣度,以此为依据确定候选推荐资源;最后,建立LSKGCN模型,深度融合用户的长期兴趣和短期兴趣,对图书资源评分,将评分高的网络图书资源推荐给用户。实验结果表明,本文方法的推荐结果具有较高的类别多样性和内容多样性。In order to effectively recommend online book resources to users,a recommendation method for online book resources based on deep fusion of interest information is proposed.Firstly,network users are clustered using the K-means clustering algorithm,and the accuracy of user clustering is improved by optimizing the clustering centers.Secondly,the degree of users'interest in book resources is calculated,and candidate recommended resources are determined based on this.Finally,an LSKGCN model is established to deeply fuse the long-term and short-term interests of users,score the book resources,and the online book resources with high scores are recommended to users.The experimental results show that the recommendation results of this method have high category diversity and content diversity.

关 键 词：兴趣信息 K均值聚类 图书资源推荐 LSKGCN模型 信息融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]