基于Hadoop平台协同过滤推荐算法  

Hadoop-Based Collaborative Filtering Recommendation Algorithm

作　　者：杨志文[1] 刘波[1] 

机构地区：[1]华南师范大学计算机学院,广州510631

出　　处：《计算机系统应用》2013年第7期108-112,共5页Computer Systems & Applications

摘　　要：针对协同过滤推荐算法在数据稀疏性及在大数据规模下系统可扩展性的两个问题,在分析研究Hadoop分布式平台与协同过滤推荐算法后,提出了一种基于Hadoop平台实现协同过滤推荐算法的优化方案.实验证明,在Hadoop平台上通过MapReduce结合Hbase数据库实现算法,能够有效地提高协同过滤推荐算法在大数据规模下的执行效率,从而能够进一步地搭建低成本高性能、动态扩展的分布式推荐引擎.In order to solve data sparsity and scalability of the Collaborative Filtering （CF） recommendation algorithm when the volume of the dataset is very large. After deeply analyzing the Hadoop distributed computing platform and the characteristic of Collaborative Filtering recommendation algorithm, the paper propose a optimization scheme on Hadoop platform. The experimental results show that it can effectively improve the execution efficiency of Collaborative Filtering recommendation algorithm in large data size, when it is realized by MapReduce with Hbase database on the Hadoop platform.And then, it contribute to build one recommendation system which is low cost, high-performance and dynamic scalability.

关 键 词：HADOOP MAPREDUCE HBASE 协同过滤推荐算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]