Title: A hybrid filtering-based network document recommendation system in cloud storage

Authors: Yuezhong Wu; Qin Liu; Changyun Li; Guojun Wang

Addresses: School of Information Science and Engineering, Central South University, Changsha, 410083, China; School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, China ' College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China ' Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory, School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, China ' School of Computer Science and Educational Software, Guangzhou University, Guangzhou, 510006, China

Abstract: Since the key requirement of users is to efficiently obtain personalised services from mass network document resources, a hybrid filtering-based network document recommendation system is designed with the method of incorporating the content-based recommendation and collaborative filtering recommendation based on the powerful and extensible storage and computing power in cloud storage. The proposed system realises the main service module on Hadoop and Mahout platforms, and processes the documents containing the information of user interests by applying AHP-based attribute weighted fusion method. Based on the network interaction, the proposed system not only has advantages on the extensible storage space and high recommendation precision but also has an essential role in realising network resources sharing and personalised recommendation.

Keywords: user interest model; collaborative filtering; recommendation system; cloud storage.

DOI: 10.1504/IJCSE.2019.103781

International Journal of Computational Science and Engineering, 2019 Vol.20 No.2, pp.269 - 279

Received: 29 Apr 2017
Accepted: 22 May 2017

Published online: 27 Nov 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article