基于mapreduce的并行推荐算法的研究与设计

Juan Yang, Han Du, Bin Wu, Xinxin Ge
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引用次数: 2

摘要

互联网技术的飞速发展带来了信息过载的问题,而推荐算法的提出被认为是解决这一问题最有效的方法。传统的推荐算法研究大多集中在准确性和多样性上。然而,在实际的工程应用中,海量数据的处理将是最严重的问题。本文提出了一种基于mapreduce编程模型的并行推荐算法,该算法运行在Hadoop平台上,在我们的系统中,我们使用mongodb作为辅助存储数据。最后,通过实验证明了该算法适用于海量数据的处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The research and design of parallel recommendation algorithm based on mapreduce
The rapid development of Internet technology has brought the problem of information overload, and recommendation algorithm is put forward and considered to be the most effective way to solve the problem. Most of the traditional research about recommendation algorithm is focused on accuracy and diversity. However, in the practical engineering application, massive data process will be the most serious problem. In this paper, we propose a parallel recommendation algorithm based on mapreduce programming model, which runs on Hadoop platform, and in our system, we use mongodb as our auxiliary storage data. Finally, we give some experiments to prove our algorithm is suitable for processing massive data.
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