Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering
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Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering Honggang Wang 1 & Weina Fu 2,3,4 Accepted: 20 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Dynamic collaborative filtering algorithm . Learning resource recommendation . Bidirectional self-equalization . Particle swarm optimization algorithm
1 Introduction Personalized services learn users’ interests and behaviors by collecting and analyzing user information, and mine the hidden interests and behavior rules of user groups, so as to formulate corresponding information filtering strategies and provide personalized active recommendation services [1]. As one of the most important technologies in personalized service, collaborative filtering recommendation is the most successful technology currently applied. Its basic idea is based on the user’s historical evaluation of resources, that is, to recommend products or
* Weina Fu [email protected] 1
College of Marxism, Fuyang Normal University, Fuyang 236032, China
2
Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410000, China
3
College of Information Science and Engineering, Hunan Normal University, Changsha 410000, China
4
Hunan Xiangjiang Artificial Intelligence Academy, Changsha 410000, China
information of interest to a specific user by using the user group preference with common experience, and the user’s preference information in the group is recorded to help other users filtering information [2]. At present, more and more people acquire knowledge through online learning. However, the traditional “teaching centered” online education mode can only provide a relatively single teaching resource, without full consideration of the distinct differences between individuals, and there is a contradiction between the unchanging learning resources and the increasing personalized learning
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