E-commerce collaborative filtering algorithm research based on cloud computing

被引：0
|
作者：
Zhao, Wenqing [1 ]
Han, Feifei [1 ]
Cai, Rui [1 ]
Wang, Dewen [1 ]
机构：
[1] North China Elect Power Univ, Sch Control & Comp Engn, Baoding 071003, Hebei Province, Peoples R China
来源：
ADVANCES IN MECHATRONICS, AUTOMATION AND APPLIED INFORMATION TECHNOLOGIES, PTS 1 AND 2 | 2014年 / 846-847卷
关键词：
Collaborative filtering; parallel; hadoop; MapReduce;
D O I：
10.4028/www.scientific.net/AMR.846-847.1566
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
With the continuous development of online shopping, a day will generate tens of thousands of consumer records. E-commerce sites want to recommend the consumers that they may be interested in the products by analyzing the consumer historical consumption data. However massive consumer records led to recommendation speed getting slow by using the traditional personalized recommendation algorithm. By researching on the collaborative filtering algorithm based on ALS and the MapReduce parallel programming model, we explore parallelization of collaborative filtering algorithm based on ALS. The experimental results show that the algorithm in this paper can improve the computing efficiency.
引用
收藏
页码：1566 / 1569
页数：4
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