关键词: 协同过滤, Hadoop, 矩阵分解, 聚类, 分布式计算

Abstract: In order to improve the data sparsity and scalability of collaborative filtering recommendation algorithms in big data, integrating matrix factorization with distributed computing, this paper proposes a distributed improved clustering collaborative filtering algorithm based on Hadoop. It uses ALS matrix factorization algorithm to fill sparse data offline. Filled matrix is clustered by improved item clustering algorithm. Then based on the clusters and similarities it creates the candidate set of recommendation. Recommendations are accomplished online. Experimental results show that the proposed algorithm can not only efficiently improve the quality of recommendation system, but also has good scalability in clouds.

Key words: collaborative filtering, Hadoop, matrix factorization, clustering, distributed computing