Construction of personalized learning service system based on deep learning and knowledge graph

被引：0

作者：

Huang M. ^{[1
]}

Xu G. ^{[2
]}

Li H. ^{[3
]}

机构：

[1] School of Information and Architecture Engineering, Anhui Open University, Anhui, Hefei

[2] School of Information and Computer, Anhui Agricultural University, Anhui, Hefei

[3] Information Technology Center, Anhui Ningguo Senior High School of China, Anhui, Xuancheng

来源：

Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期

关键词：

Collaborative Filtering; Deep Learning; Knowledge Graph; Personalized Learning; TransR Model;

D O I：

10.2478/amns.2023.2.01683

中图分类号：

学科分类号：

摘要：

With the development of information technology, it is difficult for people to find effective information from the massive Internet information, and personalized services are in short supply. This paper explores the essential association between knowledge graphs and deep learning and examines the construction process of knowledge graphs. Based on the content and collaborative filtering recommendation method, the theoretical method of personalized learning service recommendation is being studied. Knowledge graphs are introduced in collaborative filtering to accurately portray user and item vector representations. The knowledge graph is used to represent the TransR model, forming an entity space and a multivariate relationship space. The students' demand for personalized learning service systems is analyzed from the user's point of view, the personalized learning service system is constructed, and the accuracy of the personalized learning service system constructed in this paper is analyzed. The accuracy rate of DLKG is 0.1425 when K=10, which is the highest among the five models. In the effect of personalized teaching, the mean value of the metacognitive ability of the students in the experimental class is higher than that of the control class by 0.4155, 0.0261, 0.3995, 0.7967, respectively, and the experiment proves that the personalized learning service system based on the DLKG model proposed in this paper is more effective. © 2023 Min Huang et al., published by Sciendo.

引用

共 10 条

[1]

Organero M.M., Kloos C.D., Merino P.M., Personalized service-oriented e-learning environments, IEEE Internet Computing, 14, 2, pp. 62-67, (2010)

[2]

Rahima E., Van Den Berg J., Veen W., Facilitating student-driven constructing of learning environments using Web 2.0 personal learning environments, Computers & Education, 81, (2015)

[3]

Zhimin Q., Personalized distance education system based on data mining, International Journal of Emerging Technologies in Learning, 13, 7, (2018)

[4]

Personalized learning throughl: 1 technology initiatives:Implications for teachers and teaching in neoliberal times, Teaching Education, 30, 3, pp. 299-318, (2019)

[5]

Shrestha S., Pokharel M., Determining learning style preferences of learners, Journal of Computer Science Research, 3, 1, pp. 33-43, (2021)

[6]

Wang H., Wang N., Yeung D.-Y., Collaborative Deep Learning for Recommender Systems, Proceedings of the 2lth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235-1244, (2015)

[7]

Chen P., Lu Y., Zheng V.W., Et al., A System to Construct Knowledge Graph for Education, IEEE Access, pp. 31553-31565, (2018)

[8]

Chiu S.A.G., Developing a personalized recommendation system in a smart product service system based on unsupervised learning model, Computers in Industry, 128, 1, (2021)

[9]

Farahani M.G., Torkestan J.A., Rahmani M., Adaptive personalized recommender system using learning automata and items clustering, Information Systems, May, (2022)

[10]

Shi D., Wang T., Xing H., Et al., A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning, Knowledge-Based Systems, 195, (2020)

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