首都师范大学学报（自然科学版）2012，Vol.33Issue(4)：1-5,26,6.
一种解决协同过滤数据稀疏性问题的方法
In Collaborative Filtering a Method of Alleviating the Sparsity Problem
王洋 1骆力明2
作者信息
- 1. 齐齐哈尔大学经济与管理学院,齐齐哈尔161006
- 2. 首都师范大学信息工程学院,北京 100048
- 折叠
摘要
Abstract
This paper proposed a method using radial basis function ( RBF) neural network to solve the sparsity problem in collaborative filtering. The RBF neural network was constructed using a new method. The missing data in users' evaluation matrix was predicted by the RBF neural network, and the accuracy of the user similarity calculations was improved. The experimental results demonstrate that the proposed method can well-targeted recommend items for users compared with the classical collaborative filtering algorithm, and it can effectively alleviate the sparsity problem in collaborative filtering.关键词
协同过滤/稀疏性/径向基函数/平均绝对误差/神经网络Key words
collaborative filtering/sparsity/radial basis function (RBF)/mean absolute error (MAE)/neural network分类
信息技术与安全科学引用本文复制引用
王洋,骆力明..一种解决协同过滤数据稀疏性问题的方法[J].首都师范大学学报（自然科学版）,2012,33(4):1-5,26,6.
