Optimization research on collaborative filtering-based personalized educational resource recommendation algorithm

International Journal of Research Studies in Management
CollabWritive Special Issue
2025 Volume 13 Issue 3

Available Online: 25 April 2025

Author/s:

Wang, Hai
Nanning University, China

Abstract:

With the rapid development of educational informatization, various online education platforms have accumulated massive educational resources. However, this has also led to the problem of educational resource overload, making it difficult for learners to quickly find content that meets their needs and interests. To address this issue, this study focuses on the application and optimization of collaborative filtering algorithms in personalized educational resource recommendation. Aiming at the challenges faced by collaborative filtering algorithms such as data sparsity, cold start problems, and insufficient interpretability of recommendation results, this study proposes an optimized model that integrates multi-source data with graph neural networks. By introducing multi-source data such as student interest tags, course metadata, and learning behavior records, and combining a time decay factor to dynamically adjust user interest weights, the model effectively alleviates the problem of data sparsity and improves the accuracy of recommendations. Meanwhile, the use of course description text to generate embedding vectors solves the cold start problem for new users or new resources. Additionally, by constructing a course knowledge graph, the interpretability of recommendation results is significantly enhanced. Experimental results show that the optimized algorithm achieves significant improvements in evaluation metrics such as accuracy, recall, coverage, and NDCG, providing learners with more accurate and interpretable personalized educational resource recommendation services.

Keywords: collaborative filtering algorithm, personalized educational resource recommendation, data sparsity, multi-source data fusion, knowledge graph

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DOI: https://doi.org/10.5861/ijrsm.2025.25044

Cite this article:
Wang, H. (2025). Optimization research on collaborative filtering-based personalized educational resource recommendation algorithm. International Journal of Research Studies in Management, 13(3), 91-97. https://doi.org/10.5861/ijrsm.2025.25044