Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering

Wang, Honggang; Fu, Weina

doi:10.1007/s11036-020-01673-6

Cited by 43 publications

(20 citation statements)

References 21 publications

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“…Bonneville-Roussy and others pointed out that understanding the true meaning of quality education, redeveloping instructional resources for music courses, and optimizing and integrating teaching materials inside and outside the school are the guarantees for the implementation of music quality education in institution of higher learning [ 11 ]. Wang et al proposed an online resource recommendation technology that covers the differences in learning styles, knowledge levels, and learning modes of different learners to overcome the information overload problem that learners have difficulty retrieving instructional resources that meet their needs [ 12 ]. Ottone et al tried to use the BP network algorithm, which is currently widely used in computer prediction and retrieval, to optimize the detection elements of the instructional resource network sharing platform, so as to achieve the purpose of improving the user experience of the instructional resource network sharing platform [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

Lei

2022

Journal of Environmental and Public Health

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In order to overcome the problem that learners and teachers cannot find instructional resources to meet their needs and information overload in the massive resources, this article proposes and designs a music instructional resource management platform based on DCNN. This article expounds the overall goal, design principle, overall structure, and interface design of the system. At the same time, the whole construction process of a music instructional resources integration system based on DCNN is discussed in detail from the aspects of configuration of development environment, localization of platform interface, and realization of main functions of the system. In addition, through the demand analysis tool, the demand of college music instructional resources management is analyzed in detail and deeply, and the demand document is formed. This article makes an in-depth study on the categories of music instructional resources and summarizes the resource classification methods that are in line with the actual instructional activities. The experiments show that the accuracy of the proposed algorithm is improved by about 6% compared with the fuzzy clustering algorithm. At the same time, the stability of this system can reach 96.14%. This system is rich in functions and easy to use and can provide a feasible scheme for the management of instructional resources in various disciplines.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

Lei

2022

Journal of Environmental and Public Health

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“…Let the obtained resource value flow in these relationships. When the target user chosen assigns the resource value obtained by the unselected user to the user's unselected item, the target user has the unselected item of the recommended user, and the recommendation process is formed [14]. Multinode information resource recommendation process of bipartite graph network structure is shown in Figure 1.…”

Section: Similarity Calculationmentioning

confidence: 99%

A Method of Recommending Physical Education Network Course Resources Based on Machine Learning Algorithms

Wei

2021

Security and Communication Networks

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Aiming at the problem of difficult selection of physical education online course resources, a method of recommending online course resources based on machine learning algorithms is proposed. The information recommendation model is established through the expression of a collaborative filtering algorithm and resource feedback matrix. According to the feedback score of any user on the same data resource in the project set, the interest matching degree is established by comparative analysis, and the matching degree is substituted into the cosine similarity function to calculate the similarity threshold between each item and so on, calculate the similarity threshold number of all items, select the project resource that best matches the user according to the threshold number, and complete the recommendation. The experimental results show that the recommended method of physical education network curriculum resources based on machine learning algorithm is relatively excellent in recommendation accuracy and efficiency; this method can realize the innovation of higher physical education network curriculum teaching mode.

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“…According to the test results, the learner's cognitive ability was dynamically evaluated with maximum likelihood estimation (MLE) and joint probability, and a learner model was constructed based on learning preferences and cognitive ability. Wang and Fu [21] presented a personalized learning resource recommendation method based on the dynamic collaborative filtering algorithm. To recommend personalized learning resources, the Pearson correlation coefficient was adopted to compute the data similarity between learners or project resources in the network; the personalized recommendation of resources was improved by the stage-evolutionary twoway self-equilibrium mechanism; the optimal series recommendation was realized with the fuzzy adaptive binary particle swarm optimization (PSO) based on the judgement of evolutionary stat.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering Recommendation of Online Learning Resources Based on Knowledge Association Model

Jia

Yang²,

Cui

2022

Int. J. Emerg. Technol. Learn.

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Online learning platforms are prone to information overload, as they contain a huge number of diverse resources. To solve the problem, domestic and foreign scholars have focused their attention on personalized recommendation of learning resources. However, the existing studies perform poorly in the prediction of online learning paths, failing to clarify the overall knowledge system of students and the associations of resource knowledge. Therefore, this paper explores the collaborative filtering recommendation (CFR) of online learning resources (OLRs) based on knowledge association model. Firstly, the knowledge units were extracted from the semantic information of OLRs, and a knowledge association model was established for OLR recommendation. Next, a CFR algorithm was designed to couple semantic adjacency with learning interest, and used to quantify the semantic similarity of OLRs. The proposed algorithm was proved effective through experiments.

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Personalized Learning Resource Recommendation Method Based on Dynamic Collaborative Filtering

Cited by 43 publications

References 21 publications

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

[Retracted] Reset and Integration of Music Instructional Resources Using Deep Convolutional Neural Networks

A Method of Recommending Physical Education Network Course Resources Based on Machine Learning Algorithms

Collaborative Filtering Recommendation of Online Learning Resources Based on Knowledge Association Model

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