TITLE:
A Survey of Online Course Recommendation Techniques
AUTHORS:
Jinliang Lu
KEYWORDS:
Information Overload, Recommender Systems, Personalization, Online Course
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.12 No.1,
January
25,
2022
ABSTRACT: With the development of information technology, online learning has
gradually become an indispensable way of knowledge acquisition. However, with
the increasing amount of data information, it is increasingly difficult for
people to find appropriate learning materials from a large number of
educational resources. The recommender system has been widely used in various
Internet applications due to its high efficiency in filtering information,
helping users to quickly find personalized resources from thousands of
information, thereby alleviating the problem of information overload. In
addition, due to its great use value, many new researches have been proposed in
the field of recommender systems in recent years, but there are not many works
on online course recommendation at present. Therefore, this paper aims to sort
out the existing cutting-edge recommendation algorithms and the work related to
online course recommendation, so as to provide a comprehensive overview of the
online course recommender system. Specifically, we will first introduce the
main technologies and representative work used in the online course recommender
system, explain the advantages and disadvantages of various technologies, and
finally discuss the future research direction of the online course recommender
system.