Personalized learning path recommendation method based on knowledge relation mining and graph embedding driving

By constructing weighted directed graph and graph embedding technology, combined with deep learning networks, a personalized learning path recommendation model is designed, and the existing algorithms lack flexibility and dynamics in learning path recommendations are solved, and the precise mining of relationships between knowledge points and dynamic optimization of learning paths is achieved.

CN120494081AActive Publication Date: 2025-08-15CAPITAL NORMAL UNIVERSITY
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Patent Information

Application Number
CN202510589747.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-08
Publication Date
2025-08-15
Estimated Expiration
2045-05-08

AI Technical Summary

Technical Problem

The existing learning path recommendation algorithm is difficult to flexibly adapt to learners’ personalized needs, ignore the relationship between knowledge points, has high computational complexity, and lacks dynamic support for students’ learning behavior.

Method used

By mining the dependencies between knowledge points, building a weighted directed graph, combining graph embedding technology and deep learning network, designing a personalized learning path recommendation model, using the improved Apriori algorithm, DGI algorithm and Transformer architecture, optimizing the attention mechanism and long-term memory network, and dynamically capturing the relationship between learning behavior and knowledge points.

Benefits of technology

It significantly improves the ability to explore complex logical relationships between knowledge points, reduces the recommendation omission rate of sparse knowledge points, dynamically optimizes the learning path, improves the accuracy and adaptability of personalized recommendations, and is suitable for complex learning scenarios.

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Abstract

The invention belongs to the technical field of recommendation algorithms, and discloses a personalized learning path recommendation method based on knowledge relation mining and graph embedding driving, and the method comprises the following specific steps: 1, mining a knowledge point dependency relation, constructing a weighted directed graph, carrying out the data analysis on public data sets ASSISTments and Junyi, and carrying out the data analysis on the public data sets ASSISTments and Junyi; the implicit dependency relationship among knowledge points is mined through an improved Apriori algorithm, time dynamics and sequential dependency characteristics in the learning process are fully considered, and a weighted directed graph among the knowledge points is constructed according to the mined dependency relationship and the weight of the mined dependency relationship. According to the method, the implicit dependency relationship between the knowledge points is mined through an innovative method, the weighted directed graph is constructed, and a graph embedding technology is integrated into a recommendation model in combination with a unique self-defined embedding layer, so that compared with an existing recommendation method based on a simple association rule or a shallow network, the method has the advantages that the recommendation efficiency is improved; the mining and expression ability of the complex logic relation between the knowledge points is remarkably improved, and the recommendation omission rate of the sparse knowledge points can be effectively reduced.
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