Online education course recommendation algorithm based on multimodal graph attention network

Gang Li; Yuchen Hou

doi:10.1117/12.3034789

11 July 2024 Online education course recommendation algorithm based on multimodal graph attention network

Gang Li, Yuchen Hou

Author Affiliations +

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132101M (2024) https://doi.org/10.1117/12.3034789
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

With the rapid development of online learning platforms, learners have more opportunities to choose courses. However, many mainstream education platforms currently lack personalized recommendation process, and due to the diversity of course quality and content, many students find it difficult to choose more suitable courses for themselves. Therefore, personalized course recommendation plays a core role in online education platforms. The existing art education curriculum has a variety of attributes, and these attributes also include a variety of modal forms (such as course video, course pictures and course introduction); Learners themselves also have a variety of attributes, their age, educational background have an impact on the recommendation results; There is also information exchange between learners and the course, such as rating on the course, which can reflect the degree of learners' interest in the course and connect them to each other. Therefore, this paper constructs a knowledge graph, which regards learners and courses as entities and the multi-modal form of courses as course attributes. Taking learners' personal information as learner attributes and different information exchanges between learners and courses as relationships .We use the applicability of graph networks to graph structures to capture learners' preferences for different modal attributes, so as to make more accurate personalized recommendations. In this paper, MOOCCube is used as the main data set, and experiments show that the proposed algorithm is better than the baseline.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gang Li and Yuchen Hou "Online education course recommendation algorithm based on multimodal graph attention network", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132101M (11 July 2024); https://doi.org/10.1117/12.3034789

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KEYWORDS

Neural networks

Data modeling

Matrices

Online learning

Feature extraction

Video

Visualization

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