计算机技术与发展Issue(2)：26-30,5.DOI:10.3969/j.issn.1673-629X.2016.02.006
基于PCA降维的协同过滤推荐算法
Collaborative Filtering Recommendation Algorithm Based on PCA Dimension Reduction
摘要
Abstract
In the era of information overload,recommender system can help users find their interest and recommend the satisfactory infor-mation to analyze their historical behavior,so it is widely used in electronic commerce and other fields. But the user rating matrix is ex-tremely sparse in recommender systems. The sparsity of the matrix leads to great error in the calculation of similarity of recommendation algorithms,bringing about the nearest neighbor sections is not accurate,thus affecting the quality of recommendation. Aiming at the prob-lems above,a dimension reduction method based on PCA was proposed to reduce the sparsity of user rating matrix,by this method the re-main matrix retain the most representative characteristic of the user interest,so that the similarity calculation is more accurate to ensure the accuracy of the nearest neighbors,thereby improving the quality of the recommendation. The experimental results show that compared with the traditional collaborative filtering algorithm,the algorithm proposed reaches a high accuracy and coverage.关键词
主成分分析/降维/协同过滤/推荐算法Key words
PCA/dimension reduction/collaborative filtering/recommendation algorithm分类
信息技术与安全科学引用本文复制引用
李远博,曹菡..基于PCA降维的协同过滤推荐算法[J].计算机技术与发展,2016,(2):26-30,5.基金项目
国家自然科学基金资助项目(41271387) （41271387）
陕西师范大学院士创新基金资助项目(999521) （999521）
西安市科技计划基金资助项目(SF1228-3) （SF1228-3）
