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Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering

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Abstract

This paper proposes a personalized learning resource recommendation method based on dynamic collaborative filtering algorithm. Pearson correlation coefficient is used to calculate the data similarity between learning users or project resources in the network, and the unscored value is obtained. In order to solve the problems of sparse data and poor scalability in collaborative filtering algorithm, dynamic k-nearest-neighbor and Slope One algorithm are used to optimize it, and the sparsity of learning resource data in the network is analyzed according to the result of neighbor selection. The bidirectional self-equalization of stage evolution is used to improve the personalized recommendation of resource push, and the fuzzy adaptive binary particle swarm optimization algorithm based on the evolution state judgment is used to solve the problem of the optimal sequence recommendation, so as to realize the personalized learning resource recommendation. The experimental results show that the proposed method has higher matching degree and faster recommendation speed.

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Acknowledgments

This paper is supported by project of Teaching Research of Undergraduate Teaching Engineering of Fuyang Normal University with No.2019JYXM35, Provincial College Students’ innovation and Entrepreneurship Project of Anhui with No.S201910371062, Natural Science Foundation of Hunan Province with No.2020JJ5368, and Natural Science Foundation of Inner Mongolia with No.2018MS6010.

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Correspondence to Weina Fu.

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Wang, H., Fu, W. Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering. Mobile Netw Appl 26, 473–487 (2021). https://doi.org/10.1007/s11036-020-01673-6

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