Abstract—With the development of service integration
technology, online learning platforms have gathered a large
number of learning resources, causing learners to get lost in a
variety of course information and it is difficult to obtain
learning resources that match their own needs. The proposal of
personalized learning gives the problem a direction to solve.
However, current personalized learning resource
recommendation services facing problems such as excessive
candidate resources, sparse history and cold starts. In addition,
the learning resources provided also show problems of
"difficult or easy, uneven quality". For this article researches
the personalized learning recommendation model of
learner-learning resource matching. The main content includes
three parts: First, build a demand model based on learner
registration information, learning behavior and other data.
Second, analyze the access behavior of learning resources and
assess their quality. Third, calculate the matching degree
between learners and learning resources based on the demand
model and the quality information of the learning resources,
and recommend them.
Index Terms—Collaborative filtering, demand models, learning resources, personalized learning.
The authors are with the College of Information Engineering, Capital Normal University, Beijing, China (corresponding author: Shudong Zhang; e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com).
Cite: Lijuan Zhou, Feifei Zhang, Shudong Zhang, and Min Xu, "Study on the Personalized Learning Model of Learner-Learning Resource Matching," International Journal of Information and Education Technology vol. 11, no. 3, pp. 143-147, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).