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.
作者
何登平
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)
出处
《计算机工程与科学》
CSCD
北大核心
2020年第6期1089-1095,共7页
Computer Engineering & Science
关键词
多源信息
聚类
受限玻尔兹曼机
混合推荐
multi-source information
clustering
restricted Boltzmann machine
hybrid recommendation
