Collaborative filtering-based recommendation system for big data. (6th March 2020)

• Record Type: Journal Article
• Authors: Shen, Jian; Zhou, Tianqi; Chen, Lina
• Is Part Of: International journal of computational science and engineering. Volume 21:Number 2(2020)
• Page Start: 219
• Page End: 225
• Publication Date: 2020-03-06
• Subjects: big data -- collaborative filtering -- e-commerce -- movie recommendation -- MapReduce framework -- computational science; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.