Article: Design of exercise recommendation model based on clustering collaborative filtering adaptability Journal: International Journal of Computational Science and Engineering (IJCSE) 2025 Vol.28 No.3 pp.358 - 370 Abstract: The study explores the issue of insufficient personalised recommendation ability of exercise systems in online teaching. It combines clustering analysis and collaborative filtering algorithms. K-means clustering is used as the basis for clustering analysis. And the collaborative filtering algorithm is optimised from three aspects: the number of learners working together, the difference in exercise scores, and the difficulty of exercise. A clustering collaborative filtering adaptive exercise recommendation model based on similarity improvement is proposed. The study evaluates the application effectiveness of the model through simulation experiments. The experimental results show that the MAE values formed by the designed algorithm under changes of the nearest neighbours are the lowest among the comparison algorithms, proving its superiority. In the comparison of indicators, the accuracy, recall, and F1 values of the algorithm are all the highest among the comparison algorithms, further verifying its effectiveness. Stability analysis shows that in both sets, the accuracy of the research design model reaches above 0.87, indicating that the model has high stability and accuracy. From this, the model designed in the study has advantages in recommendation effectiveness, which can help students improve learning effectiveness and provides a new approach for learning assistance systems. Inderscience Publishers - linking academia, business and industry through research

Title: Design of exercise recommendation model based on clustering collaborative filtering adaptability

Authors: Chaoyang Shi; Zhen Zhang

Addresses: Department of Public Education, Zhengzhou Yellow River Nursing Vocational College, Zhengzhou, 450066, China ' School of Electronics and Information, Zhengzhou University of Aeronautics, Zhengzhou, 450046, China

Abstract: The study explores the issue of insufficient personalised recommendation ability of exercise systems in online teaching. It combines clustering analysis and collaborative filtering algorithms. K-means clustering is used as the basis for clustering analysis. And the collaborative filtering algorithm is optimised from three aspects: the number of learners working together, the difference in exercise scores, and the difficulty of exercise. A clustering collaborative filtering adaptive exercise recommendation model based on similarity improvement is proposed. The study evaluates the application effectiveness of the model through simulation experiments. The experimental results show that the MAE values formed by the designed algorithm under changes of the nearest neighbours are the lowest among the comparison algorithms, proving its superiority. In the comparison of indicators, the accuracy, recall, and F1 values of the algorithm are all the highest among the comparison algorithms, further verifying its effectiveness. Stability analysis shows that in both sets, the accuracy of the research design model reaches above 0.87, indicating that the model has high stability and accuracy. From this, the model designed in the study has advantages in recommendation effectiveness, which can help students improve learning effectiveness and provides a new approach for learning assistance systems.

Keywords: clustering; collaborative filtering; online learning; exercise; recommendation; similarity.

DOI: 10.1504/IJCSE.2025.146075

International Journal of Computational Science and Engineering, 2025 Vol.28 No.3, pp.358 - 370

Received: 03 Aug 2023
Accepted: 23 Jan 2024
Published online: 06 May 2025 *

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Title: Design of exercise recommendation model based on clustering collaborative filtering adaptability

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