基于多源信息聚类和IRC-RBM的混合推荐算法  

A hybrid recommendation algorithm based on multi-source information clustering and IRC-RBM

作　　者：何登平[1,2,3] 张为易 黄浩 HE Deng-ping;ZHANG Wei-yi;HUANG Hao(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065;Research Center of New Telecommunication Technology Applications,Chongqing University of Posts and Telecommunications,Chongqing 400065;Chongqing Information Technology Designing Co.,Ltd.,Chongqing 401121,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]重庆邮电大学通信新技术应用研究中心,重庆400065 [3]重庆信科设计有限公司,重庆401121

出　　处：《计算机工程与科学》2020年第6期1089-1095,共7页Computer Engineering & Science

摘　　要：针对协同过滤存在的数据稀疏性问题,提出了融合多源信息聚类和IRC-RBM的混合推荐算法。首先以用户信任度和项目时间权重作为聚类依据,利用最小生成树的K-means聚类算法对用户进行聚类分析,生成K个相似用户集合,在聚类分析的基础上进行评分预测;最后通过线性加权的方式,把聚类后评分矩阵和IRC-RBM模型生成的评分矩阵进行加权融合,用Top-N进行推荐。实验结果表明,相比较传统的推荐算法,该混合算法在准确率上有了显著的提升。To solve the problem of data sparsity in collaborative filtering,this paper proposes a hybrid recommendation algorithm combining multi-source information clustering and IRC-RBM.Firstly,this algorithm takes user trust and project time weight as the clustering basis,uses the K-means clustering algorithm of minimum spanning tree to carry out clustering analysis on users,generates K similar user sets,and conducts scoring prediction on the basis of clustering analysis.Finally,the scoring matrix after clustering and the scoring matrix generated by IRC-RBM model are weighted and fused by linear weighting,and Top-N is used for recommendation.Experimental results show that the proposed hybrid recommendation algorithm significantly improves the accuracy in comparison to the traditional recommendation algorithm.

关 键 词：多源信息 聚类 受限玻尔兹曼机 混合推荐 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]