Personalized learning path recommendation method and system based on knowledge graph mining

Abstract

The invention belongs to the field of learning path recommendation, and provides a personalized learning path recommendation method and system based on knowledge graph mining, solves the problem of poor matching between a learning path obtained by a traditional method and a corresponding learner, and can improve the learning path recommendation precision of the learner, so that the matching degreebetween the learning path and the learner is higher. The personalized learning path recommendation method comprises the steps that a learning path model is constructed based on a knowledge graph, andthen the knowledge graph is mapped into a general learning path graph; performing personalized processing on the general learning path diagram according to the learner features to obtain an alternative learning path diagram; taking all learning paths in the alternative learning path graph as initial learning paths; and generating a new learning path by using the genetic algorithm and the adaptivevalue of the learning path, and when a preset number of iterations is reached or the adaptive values of all generated new learning paths are greater than a preset threshold, stopping iteration and screening out the learning path with the highest adaptive value as a recommendation result.

Description

technical field [0001] The invention belongs to the field of online education data processing, and in particular relates to a personalized learning path recommendation method and system based on knowledge map mining. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the rapid development of Internet technology and the continuous improvement of public learning concepts, online learning activities using the Internet as a medium have become a very common social activity. However, the accompanying massive knowledge and data have also brought serious problems of "information overload" and "learning trek" to the field of online education. With the extensive development of knowledge graph technology in the fields of intelligent search, intelligent translation, and personalized recommendation, generating intelligent solutions and personalized ...

AI Technical Summary

Problems solved by technology

[0004] The inventors found that, on the one hand, the existing learning path recommendation methods do not consider the learner's own characteristics (such as learner type, learning background, learning objectives, etc.) and individual needs, so that the learning path obtained by the traditional method has certain limitations. On the other hand, the learning path optimization process only considers the knowledge points that the learners have already learned, and the finally selected learning paths cannot be prioritized, and cannot achieve the purpose of personalized recommendation of the learning paths, which may reduce the learning ability of the learners. efficiency

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