Title: Collaborative filtering-based recommendation system for big data
Authors: Jian Shen; Tianqi Zhou; Lina Chen
Addresses: Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China; State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; Guangxi Key Laboratory of Cryptography and Information Security, Guilin, China ' School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China ' Guizhousheng College of Electronics and Information, Guizhou, China
Abstract: 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 users' historical behaviour data, so as to explore the users' 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 datasets. The experimental results show that the system can achieve high efficiency and reliability in large datasets.
Keywords: big data; collaborative filtering; e-commerce; movie recommendation; MapReduce framework; computational science.
International Journal of Computational Science and Engineering, 2020 Vol.21 No.2, pp.219 - 225
Received: 06 Sep 2017
Accepted: 08 Apr 2018
Published online: 11 Mar 2020 *