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

Jia, Henan; Yang, Lei; Cui, Bo

doi:10.3991/ijet.v17i02.29013

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…In order to test the quality of the proposed algorithm, the proposed algorithm based on the improved collaborative filtering model is compared with the literature [ 23 ], literature [ 24 ], and literature [ 25 ]. The number of adjacent recommendation students is set as 5, and the value is increased by 15 until it is 50.…”

Section: Results Analysis and Discussionmentioning

confidence: 99%

The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model

Geng

2022

Computational Intelligence and Neuroscience

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In the huge number of online university education resources, it is difficult for learners to quickly locate the resources they need, which leads to “information trek.” Traditional information recommendation methods tend to ignore the characteristics of learners, who are the main subjects of education. In order to improve the recommendation accuracy, a recommendation algorithm based on improved collaborative filtering model is proposed in this paper. Firstly, according to the student behavior data, consider the behavior order to create the behavior graph and behavior route. Then, the path of text type is vectorized by the Keras Tokenizer method. Finally, the similarity between multidimensional behavior path vectors is calculated, and path collaborative filtering recommendations are performed for each dimension separately. The MOOC data of a university in China are introduced to experimentally compare the algorithm of the article as well as the control group algorithm. The results show that the proposed algorithm takes better values in evaluation indexes, thus verifying that this algorithm can improve the effectiveness of innovation and entrepreneurship education resources recommendation in universities.

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Section: Results Analysis and Discussionmentioning

confidence: 99%

The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model

Geng

2022

Computational Intelligence and Neuroscience

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“…Online learning platforms are prone to information overload, as they contain a large number of diverse resources. To address this problem, Jia et al [24] explored collaborative filtering recommendation (CFR) for online learning resources based on a knowledge association model. Knowledge units were extracted from the semantic information of online learning resources (OLRs) to build a knowledge association model for OLR recommendations.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

Niu¹

2023

Int. J. Emerg. Technol. Learn.

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Knowledge networks play an important role in the process of knowledge acquisition and sharing by students. An analysis of their complex structural features is required for the connectivity between students and knowledge. Existing research lacks insight into the internal structural features of knowledge networks constructed from expertise. There is also a lack of effective methods for constructing personalised knowledge networks for students' cognitive states. This paper analyses the categories and structures of expertise for students' cognitive states, and presents in detail a grey prediction algorithm to identify students' cognitive states. Then, the paper presents a typological description of the knowledge nodes in the expertise network for students' cognitive states, and analyses the knowledge network structure from the perspectives of paths and statistical properties. After that, the paper gives a method for analysing the knowledge flow of the expertise network. The experimental results validate the effectiveness of the proposed method.

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“…Video has been welcomed by students as an important type of learning resources for its simple operation and abundant information. More and more students are choosing to watch online learning videos from online learning platforms in various forms of intelligent terminals [8][9][10][11][12]. However, as the number of online videos on video websites grows rapidly, the problem of information overload becomes more serious.…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

Liu¹

2023

Int. J. Emerg. Technol. Learn.

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Video is an important carrier of all kinds of information on the Internet. More and more students choose to watch online learning videos from online learning platforms with various forms of intelligent terminals. To allow students to get the videos which they really need and are interested in from the mass videos, it is necessary to improve the existing personalized recommendation algorithm. Content-based recommendation method, collaborative filtering-based recommendation method and hybrid recommendation method often have the problems of loss of feature information or cocoon. To this end, this paper studies the personalized recommendation service of media educational resources based on multi-dimensional user features. This paper constructs a multi-feature candidate set based on student-video interaction, extracts and splices category feature, label feature and semantic feature of Internet media educational videos, and generates the representation of students’ learning preference feature. Based on the improved forward and backward recurrent neural network, this study constructs a recommendation model of Internet media educational videos oriented to students' preference points. The experimental result verifies the validity of the model.

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

Cited by 7 publications

References 18 publications

The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model

The Recommendation System of Innovation and Entrepreneurship Education Resources in Universities Based on Improved Collaborative Filtering Model

An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

Personalized Recommendation Service of Educational Media Resources Based on Multi-dimensional Feature Fusion

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