计算机应用研究2018，Vol.35Issue(2)：349-353,5.DOI:10.3969/j.issn.1001-3695.2018.02.007
基于TimeRBM和项目属性聚类的混合协同过滤算法
Hybrid collaborative filtering algorithm based on TimeRBM and item attribute clustering
杜丹琪 1周凤1
作者信息
- 1. 贵州大学计算机科学与技术学院,贵阳550025
- 折叠
摘要
Abstract
In order to overcome the disadvantages of the restricted Boltzmann machine (RBM) for collaborative filtering algorithm,which ignored the fact that user's interests varied over time and only used the serious sparse user rating data.Firstly,this paper proposed a user-based RBM model with time information:TimeRBM,by adding the time information bias term into the existing RBM model.Secondly,it proposed a method of clustering based on the attributes of the item for rating prediction.Finally,it weighted and fused the two prediction results obtained by the TimeRBM model and the item attribute clustering,thus built an efficient hybrid algorithm.Experimental results on the benchmark dataset show that this hybrid algorithm can improve the prediction accuracy of the recommendation system.关键词
受限波尔茨曼机/时间函数/TimeRBM/项目属性聚类Key words
RBM/time function/TimeRBM/item attribute clustering分类
信息技术与安全科学引用本文复制引用
杜丹琪,周凤..基于TimeRBM和项目属性聚类的混合协同过滤算法[J].计算机应用研究,2018,35(2):349-353,5.
