软件导刊Issue(2)：74-77,4.DOI:10.11907/rjdk.104179
协同过滤算法改进及研究
Collaborative Filtering Algorithm Improvements and Research
苏杨茜1
作者信息
- 1. 中南民族大学计算机科学学院，湖北武汉430074
- 折叠
摘要
Abstract
Targeting the sparsity problem of collaborative filtering ,an improved method- -BAS algorithms is proposed . The algorithm combines the Bayesian measure of dimensionality reduction and singular value decomposition method ,ob‐tained the neighbors of active users based on traditional singular value decomposition method ,to get the final predicted value w hich provided to users through improved similarity measure .Experimental results show that the method used in the data set can effectively alleviate the data sparseness problem ,and can improve the recommendation accuracy ,and the recommendation quality of engine to a certain extent .关键词
推荐引擎/协同过滤算法/数据稀疏/奇异值分解Key words
Recommendation Engine/Collaborative Filtering Algorithm/Data Sparse/Singular Value Decomposition分类
信息技术与安全科学引用本文复制引用
苏杨茜..协同过滤算法改进及研究[J].软件导刊,2015,(2):74-77,4.
