计算机工程2012，Vol.38Issue(21)：56-58,66,4.DOI:10.3969/j.issn.1000-3428.2012.21.015
基于近邻评分填补的协同过滤推荐算法
Collaborative Filtering Recommendation Algorithm Based on Neighbor Rating Imputation
摘要
Abstract
Data sparsiry influences the recommendation quality of collaborative filtering algorithm. To address this problem, a new hybrid collaborative filtering algorithm based on neighbor rating imputation is proposed. The dimensions of original rating matrix are reduced by Principal Component Analysis(PCA), which can reduce the computational complexity. Singular Value Decomposition(SVD) is used to impute missing ratings of the neighbors, which can alleviate the data sparsiry. Experiments are carried out on MovieLens dataset, and the results show that the algorithm has higher the recommendation efficiency.关键词
推荐系统/协同过滤/主成分分析/近邻评分填补/稀疏性Key words
recommendation system/ collaborative filtering/ Principal Component Analysis(PCA)/ neighbor rating imputation/ sparsity分类
信息技术与安全科学引用本文复制引用
冷亚军,梁昌勇,陆青,陆文星..基于近邻评分填补的协同过滤推荐算法[J].计算机工程,2012,38(21):56-58,66,4.基金项目
国家自然科学基金资助项目(71271072) （71271072）
高等学校博士学科点专项科研基金资助项目(20110111110006) （20110111110006）
教育部人文社会科学研究青年基金资助项目(09YJC630055) （09YJC630055）
