• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2015, Vol. 37 ›› Issue (08): 1444-1449.

• 论文 • 上一篇    下一篇

基于Mahout的新用户推荐算法的设计与实现

高献卫,师智斌   

  1. (中北大学计算机与控制工程学院,山西 太原 030051)
  • 收稿日期:2014-11-24 修回日期:2015-05-15 出版日期:2015-08-25 发布日期:2015-08-25

Design and implementation of a new user
recommendation algorithm based on Mahout  

GAO Xianwei,SHI Zhibin   

  1. (School of Computer Science and Control Engineering Technology,North University of China,Taiyuan 030051,China)
  • Received:2014-11-24 Revised:2015-05-15 Online:2015-08-25 Published:2015-08-25

摘要:

为了解决大数据背景下新用户因没有历史数据而导致推荐难和推荐效率低等问题,提出将基于Mahout的协同过滤算法与基于MapReduce的Top N算法相结合的技术方法,来实现新用户推荐算法,从而构建新用户推荐系统的架构,并对Hadoop Top N算法以及Mahout中协同过滤算法进行设计与实现。理论分析和实验验证表明,该新用户推荐算法在推荐效率、对大规模数据处理的伸缩性以及推荐质量上都明显优于单独使用协同过滤算法的新用户推荐。

关键词: 新用户推荐, Mahout, 推荐系统, Hadoop, 大数据

Abstract:

Recommendation for new users in big data era is difficult and the efficiency is very low due to the lack of historical data. In order to solve these problems, we propose a new user recommendation algorithm, which combines the collaborative filtering algorithm based on the Mahout and the Top N algorithm based on the MapReduce. We build a new user recommendation system architecture, design and implement the Hadoop Top N algorithm and the collaborative filtering algorithm in the Mahout. Theoretical analysis and experimental results show that the proposed recommendation algorithm for big data processing has better recommended efficiency, scalability and quality than  the collaborative filtering algorithm.

Key words: new user recommendation;Mahout;recommendation system;Hadoop;big data