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Open Access

Data-Driven Artificial Intelligence Recommendation Mechanism in Online Learning Resources

Lina Yang1( ), Yawen Yu2, Yonghong Wei3
School of Communication, Tianjin Foreign Studies University, Tianjin 300204, China
School of Education, The University of Hong Kong, Hong Kong 999077, China
School of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin 300457, China
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Abstract

This study attempts to reveal the intelligent recommendation mechanism of online learning resources. The “multimodal data fusion” aspect is adopted, illustrating online learning resource modeling, learning preference modeling, learning preference evolution, and referral engine smart design. Multimodal learning resources are key constituents of multidimensional intelligent recommendation services. The learner model that integrates multimodal preference data is the key to implementing smart recommendations. Learning preference evolution is a key factor in sustaining learner model robustness. The intelligent recommendation engine is the technical guarantee for developing an intelligent recommendation. Providing learning resources is an important part of online learning services, which mediate online learning processes. Intelligent recommendation, as an effective personalized servicing strategy, recommends differentiated, personalized, and precise learning resources to learners, thereby promoting the effectiveness of online learning performance. This study constructs a framework that reveals an intelligent recommendation mechanism for online learning resources.

Keywords

data-drivenonline learning resourceintelligent recommendation

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International Journal of Crowd Science
Volume 6 Issue 3,
September 2022
Pages 150-157
DOI: 10.26599/IJCS.2022.9100020
Cite this article:
Yang L, Yu Y, Wei Y. Data-Driven Artificial Intelligence Recommendation Mechanism in Online Learning Resources. International Journal of Crowd Science, 2022, 6(3): 150-157. https://doi.org/10.26599/IJCS.2022.9100020

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Received: 16 March 2022
Revised: 11 May 2022
Accepted: 11 May 2022
Published: 09 August 2022
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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