计算机技术与发展Issue(2)：55-59,5.DOI:10.3969/j.issn.1673-629X.2015.02.013
基于矩阵分解的协同过滤算法的并行化研究
Parallelized Research on Collaborative Filtering Algorithm Based on Matrix Factorization
摘要
Abstract
Collaborative filtering algorithm based on matrix factorization is a collaborative filtering recommendation technique proposed in recent years. In the process of recommendation each prediction depends on the collaboration of the whole known rating set and the feature matrices need huge storage. So the recommendation with only one node will meet the bottleneck of time and resource. Through in-depth study on the principle and feature of current parallel implementation of a collaborative filtering algorithm based on ALS ( Alternating-Least-Squares) ,get the reason why the computing efficiency of the implementation of traditional iterative algorithm on Hadoop is very low. According to the idea of iterative MapReduce,some methods such as loop-aware scheduling algorithm,static data caching,job loop controlling,fixed point detecting are proposed. The experiment on Netflix data set shows that the iterative MapReduce has improved the parallel computing efficiency of collaborative filtering algorithm based on ALS.关键词
ALS算法/协同过滤/Hadoop/迭代式MapReduceKey words
alternating least squares/collaborative filtering/Hadoop/iterative MapReduce分类
信息技术与安全科学引用本文复制引用
王全民,苗雨,何明,郑爽..基于矩阵分解的协同过滤算法的并行化研究[J].计算机技术与发展,2015,(2):55-59,5.基金项目
国家自然科学基金资助项目(61272500) （61272500）
