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.
Highlights
1.1 Background and related work Recent years have witnessed the rapid development of computer and the internet, changing many aspects in people’s daily lives such as the way of receiving education
This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target Massive Open Online Courses (MOOCs) users
We compare the efficiencies of the two algorithms using true data set from MOOC College
Summary
1.1 Background and related work Recent years have witnessed the rapid development of computer and the internet, changing many aspects in people’s daily lives such as the way of receiving education. People all over the world can get access to massive quality courses through the internet. Massive online courses are produced by instructors who are professional in specific fields. International Journal of Crowd Science Vol 1 No 3, 2017 pp. Published in the International Journal of Crowd Science. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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