E3S Web Conf.
Volume 214, 20202020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|Number of page(s)||5|
|Section||Big Data Analysis Application and Energy Consumption Research|
|Published online||07 December 2020|
Design of personalized recommendation system for online learning resources based on improved collaborative filtering algorithm
1 Guangzhou Nanyang Polytechnic Guangzhou, 510900, China
2 Nanfang College of Sun Yat-sen University Guangzhou, 510900, China
In recent years, under the guidance of the educational concept of equality and sharing, universities at home and abroad have increased the development and application of online course learning resources. In China, online open courses are open to all learners on the platform of major portals. Due to the increasing number of online courses, it is increasingly difficult for learners to find the content they are interested in on the website. In addition, the traditional collaborative filtering has the problems of sparse data, cold start, and low accuracy of recommendation results, etc. Therefore, the personalized recommendation system studied in this paper adds the collaborative filtering recommendation technology of user and project attributes. The recommendation system can actively discover the interest of learners according to their behavior characteristics, and provide them with online learning resources of interest, and improve the accuracy of the recommendation results by improving the collaborative filtering algorithm. In this paper, personalized recommendation technology is applied to online course website, aiming at providing personalized, automated and intelligent recommendation system for online learners.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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