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Author:

Geng, Chuanwei (Geng, Chuanwei.) | Zhang, Jian (Zhang, Jian.) | Guan, Lianzheng (Guan, Lianzheng.)

Indexed by:

EI Scopus

Abstract:

This paper provides a collaborative filtering recommendation algorithm based on the combination of item similarity and alternating least square collaborative filtering based on the Spark platform. This is a recommended method to improve the efficiency of prediction calculations and reduce system response time. In order to solve the problem of model inaccuracy caused by the sparse data of the existing collaborative filtering recommendation scheme, which leads to the inaccuracy of recommending suitable online education and teaching resources to different users, the present invention uses the least squares collaborative filtering recommendation algorithm on the Spark big data analysis platform Optimize and use, and then use parallel methods to increase the amount of work completed per unit time and the accuracy of recommendations, and solve the problem of inaccurate recommendation of teaching resources. © Published under licence by IOP Publishing Ltd.

Keyword:

E-learning Least squares approximations Collaborative filtering

Author Community:

  • [ 1 ] [Geng, Chuanwei]Department of Information, Beijing University of Technology, Beijing, China
  • [ 2 ] [Zhang, Jian]Department of Information, Beijing University of Technology, Beijing, China
  • [ 3 ] [Guan, Lianzheng]Department of Information, Beijing University of Technology, Beijing, China

Reprint Author's Address:

  • [geng, chuanwei]department of information, beijing university of technology, beijing, china

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Source :

ISSN: 1742-6588

Year: 2021

Issue: 4

Volume: 1865

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 17

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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