A Parallel Implementation of Singular Value Decomposition based on Map-Reduce and PARPACK

摘要：

In the e-commerce on the Web,recommender systems become a powerful technology for extracting valuable information from its customer databases. These systems also help customers find products they want to buy from a business sites. Singular Value Decomposition(SVD) is a useful technology to speedup the recommendations with very fast online performance, requiring just a few simple arithmetic operations. Unfortunately, computing the SVD of a large scale matrix is very expensive. In this paper, we propose to parallelize the SVD algorithm to run on distributed computers.Our parallel algorithm employs a parallel ARPACK algorithm to perform parallel eigenvalue decomposi-tion.Experimental results show that the proposed method can significantly speed up the SVD computation cost while providing comparable prediction quality.

关键词： Singular Value Decomposition Map-Reduce PARPACK

作者: Yaguang Ding Guofeng Zhu Chenyang Cui Jian Zhou Liang Tao

作者单位: School of Computer Science and Technology,Anhui University,Hefei,China

会议类型: 国际会议

会议名称: 2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

会议地点: 哈尔滨

会议语种:英文

页码: 739-741

在线出版日期: 2011-12-24（万方平台首次上网日期，不代表论文的发表时间）