Movie Recommendation System Employing the User-based CF in Cloud Computing

被引：17
|
作者：
Zhou, Tianqi [1 ]
Chen, Lina [2 ]
Shen, Jian [1 ]
机构：
[1] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
[2] Guizhousheng Coll Elect & Informat, Guizhou 558000, Peoples R China
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 2 | 2017年
基金：
中国国家自然科学基金;
关键词：
Big data; collaborative filtering; e-commerce; movie recommendation; MapReduce framework;
D O I：
10.1109/CSE-EUC.2017.194
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Collaborative filtering algorithm is widely used in the recommendation system of e-commerce website, which is based on the analysis of a large number of user's historical behavior data, so as to explore the user's interest and recommend the appropriate products to users. In this paper, we focus on how to design a reliable and highly accurate algorithm for movie recommendation. It is worth noting that the algorithm is not limited to film recommendation, but can be applied in many other areas of e-commerce. In this paper, we use Java language to implement a movie recommendation system in Ubuntu system. Benefiting from the MapReduce framework and the recommendation algorithm based on items, the system can handle large data sets. The experimental results show that the system can achieve high efficiency and reliability in large data sets.
引用
收藏
页码：46 / 50
页数：5
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