A Comparative Study of Recommendation Algorithms in E-Commerce

被引：0
|
作者：
Chavan, Reshma [1 ]
Mukhopadhyay, Debajyoti [1 ]
机构：
[1] Maharashtra Inst Technol, Dept Informat Technol, Pune, Maharashtra, India
来源：
2017 INTERNATIONAL CONFERENCE ON I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD) (I-SMAC) | 2017年
关键词：
E-Commerce; Recommender System; Hadoop; MapReduce; Data Mining;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The recommender systems are used extensively around the world to increase sales and profits. A proper recommender system improves overall user shopping experience. In the e-commerce industry, this approach helps acquire potential market opportunities. This paper focuses on the comparison of different recommendation algorithms and provides a guideline to choose the best algorithm. Better and appropriate recommendations can be achieved by analysing a large amount of data by using Hadoop MapReduce framework, along with appropriate GUI.
引用
收藏
页码：163 / 168
页数：6
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