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Research paper | Open Access

An improved algorithm for personalized recommendation on MOOCs

Yuqin Wang1, Bing Liang1, Wen Ji1( ), Shiwei Wang2, Yiqiang Chen1
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Weihai Yuanhang Technology Development Co. Ltd, Shandong, China
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Abstract

Purpose

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

Design/methodology/approach

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

Findings

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

Originality/value

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

Keywords

MOOCCollaborative filtering algorithmMulti-attribute weight algorithmPersonalized recommendation

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International Journal of Crowd Science
Volume 1 Issue 3,
September 2017
Pages 186-196
DOI: 10.1108/IJCS-08-2017-0021
Cite this article:
Wang Y, Liang B, Ji W, et al. An improved algorithm for personalized recommendation on MOOCs. International Journal of Crowd Science, 2017, 1(3): 186-196. https://doi.org/10.1108/IJCS-08-2017-0021

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Received: 22 August 2017
Accepted: 09 September 2017
Published: 04 September 2017
© The author(s)

Yuqin Wang, Bing Liang, Wen Ji, Shiwei Wang and Yiqiang Chen. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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