Abstract: Traditional collaborative filtering recommendation technology works poor under the environment of bigdata. To solve this problem, a personalized recommendation method based on cloud-computing technology is proposed. In this method, the large dataset and recommended calculation will be decomposed into multiple computers for parallel processing. It uses the open source framework Hadoop to establish a parallel recommendation engine on the basis of the classical ItemCF algorithm with MapReduce technology. The effectiveness of this system has already been verified on English training platforms by recommending learning resources. Experimental results indicate that the use of cloud-computing technology in the cluster to process massive data can significantly improve the scalability of Recommender Systems.

Key words: recommender systems, item-based collaborative filtering, MapReduce, ItemCF-MR algorithm, learning resources recommend

摘要： 传统的协同过滤推荐技术在大数据环境下存在一定的不足。针对该问题，提出了一种基于云计算技术的个性化推荐方法：将大数据集和推荐计算分解到多台计算机上并行处理。在对经典ItemCF算法MapReduce化后，建立了一个基于Hadoop开源框架的并行推荐引擎，并通过在已商用的英语训练平台上进行学习推荐工作验证了该系统的有效性。实验结果表明，在集群中使用云计算技术处理海量数据，可以大大提高推荐系统的可扩展性。

关键词: 推荐系统, 基于物品的协同过滤, MapReduce, ItemCF-MR算法, 学习资源推荐