Knowledge graph embedding-based learning resource recommendation method and system

Abstract

The invention provides a knowledge graph embedding-based learning resource recommendation method and system. The method comprises the steps of determining a learning resource knowledge graph; constructing learner features based on social attributes, knowledge levels, learning styles and learning concentration degrees of the learner; combining the learner features with the learning resource knowledge graph to obtain a multi-modal knowledge graph; constructing a learning resource recommendation model into which the knowledge graph is embedded, inputting the multi-modal knowledge graph, the resources preferred by the learner and the target learning resource into the learning resource recommendation model, extracting a path between the resources preferred by the learner and the target learning resource, scoring the target learning resource, judging whether the target learning resource meets the requirements of the learner or not, and recommending the learning resource meeting the requirements of the learner to the learner based on a scoring result. According to the method, the influence factors of the learning state of the learner and the incidence relation between the resources are comprehensively considered for learning resource recommendation, and the actual requirements of the learner can be met.

Description

technical field [0001] The invention belongs to the field of resource recommendation, and more specifically relates to a learning resource recommendation method and system based on knowledge map embedding. Background technique [0002] The personalized learning resource recommendation system recommends resources that meet user needs based on user characteristics and interest preferences, and is an important link in personalized learning. Existing research on personalized recommendation algorithms mainly includes collaborative filtering-based recommendation, content-based recommendation, and hybrid recommendation. [0003] Collaborative filtering is a recommendation method based on users, items, and historical interactions between them. Boban et al. used clustering and sequential pattern mining algorithms to complete personalized recommendations for learners. García et al. introduced a collaborative filtering educational data mining algorithm based on association rules, whi...

AI Technical Summary

Problems solved by technology

[0008] In view of the deficiencies of the prior art, the purpose of the present invention is to provide a learning resource recommendation method and system based on knowledge graph embedding, which aims to solve the problem that the existing learning resource recommendation method still uses the resource as an independent individual for feature extraction. It does not effectively consider the relevance and timing of learning resources, and cannot meet the actual needs of learning in order

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