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Knowledge graph embedding-based learning resource recommendation method and system

A technology of learning resources and knowledge map, applied in the field of learning resource recommendation method and system based on knowledge map embedding, can solve the problems of inability to meet the actual needs of learning in order, and the relevance of learning resources is not effectively considered, and achieve accurate Recommended effect

Active Publication Date: 2021-12-03
HUAZHONG NORMAL UNIV
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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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  • Knowledge graph embedding-based learning resource recommendation method and system
  • Knowledge graph embedding-based learning resource recommendation method and system
  • Knowledge graph embedding-based learning resource recommendation method and system

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Embodiment Construction

[0047] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0048] The graph embedding algorithm is a network representation method, and the present invention intends to introduce the graph embedding method as a feature representation method of the knowledge path. They have been used in many practical applications. In the past few years, a lot of research has focused on designing new embedding algorithms. These methods can be grouped into three broad categories: 1) factorization methods such as LINE attempt to approximate the factorization of the adjacency matrix while preserving first- and second-order approximations; 2) deep learning methods enhance the ...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F16/36G06N3/04G06N3/08
CPCG06F16/9535G06F16/367G06N3/049G06N3/08G06N3/045
Inventor 张浩刘三女牙黄涛戴志诚周东波童航李耀鹏闵远东
Owner HUAZHONG NORMAL UNIV
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