[1]李程,曹菡,师军. 基于MapReduce的混合推荐算法及应用[J].计算机技术与发展,2016,26(04):74-77.
　LI Cheng,CAO Han,SHI Jun. Hybrid Recommendation Algorithm Based on MapReduce and Its Application[J].,2016,26(04):74-77.
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 基于MapReduce的混合推荐算法及应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年04期
页码:
74-77
栏目:
智能、算法、系统工程
出版日期:
2016-04-10

文章信息/Info

Title:
 Hybrid Recommendation Algorithm Based on MapReduce and Its Application
文章编号:
1673-629X（2016)04-0074-04
作者:
 李程; 曹菡; 师军
 陕西师范大学 计算机科学学院,
Author(s):
 LI Cheng; CAO Han; SHI Jun
关键词:
 ; MapReduce; Hadoop; 混合推荐算法; 云计算
Keywords:
 ; MapReduce; Hadoop; hybrid recommendation algorithm; cloud computing
分类号:
TP311
文献标志码:
A
摘要:
 针对基于项目与基于用户两种传统协同过滤算法的不足，文中结合基于用户以及基于项目的两种传统协同过滤算法，并加以合理改进，提出了一种新型的混合型并行推荐算法。通过对新算法MapReduce编译，使新算法能够在Hadoop云平台下顺利运行。在可以利用以基于用户的方法为基础划定出定量的邻居范围，保证了推荐的个性化，同时，利用基于项目的协同过滤算法进行推荐，最终根据综合因素调整评分预测方法得出符合实际的推荐结果。实验结果表明，在数据量相对较大时新算法不仅在处理速度上表现更加优越，而且明显提高了推荐精确度。同时文中将该算法应用在西安本土旅游推荐服务上，针对西安市几大景点进行推荐，使新算法的准确性在实际应用中得到验证。
Abstract:
 For the shortcomings of traditional project-based and user-based collaborative filtering algorithm,a new parallel recommenda-tion algorithm is proposed,combined user-based with project-based collaborative filtering algorithm and improved them. Through Ma-pReduce compilation,the new algorithm can run in Hadoop cloud platform. To guarantee the personalized recommendation,it can take ad-vantages of the collaborative filtering algorithm based on user defined a certain number of neighbors. At the same time,the project-based collaborative filtering algorithm is used to recommend. Finally,according to the comprehensive adjusted score prediction method,the rec-ommended results are obtained. The experimental results show that the algorithm becomes more superior in the case of a large number of processing speed,and improves the accuracy of recommendation. Simultaneously,the algorithm is applied in local tourism of Xi’ an refer-ral service for several major attractions to recommend. The accuracy of the new algorithm has been verified in practical applications.

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更新日期/Last Update: 2016-06-16