Study on Recommendation Algorithm with Matrix Factorization Method Based on MapReduce
基于MapReduce的矩阵分解推荐算法研究

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. A MapReduce-based matrix factorization recommendation algorithm was proposed to solve this problem. The big feature matrices were shared by Hadoop distributed cache and MapFile techniques.The MapReduce algorithm could also handle multi-? situation. The experiment on Netflix data set shows that the MapReduce-based algorithm has high speedup and improves the efficiency of collaborative filtering.