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Personalized learning resource recommendation method based on learner preference modeling

A technology of learning resources and recommendation methods, applied in the field of learner portrait construction for learning resource recommendation, can solve the problem that learner preferences cannot be reasonably expanded, and the different effects of target courses are not considered, so as to achieve rationalization of correlation results and ensure comprehensive Performance and accuracy, the effect of simplifying storage

Active Publication Date: 2020-07-28
GUILIN UNIV OF ELECTRONIC TECH
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AI Technical Summary

Problems solved by technology

[0007] What the present invention aims to solve is the sparsity of learner-course interaction data in the current personalized recommendation method based on learner preference modeling, learner preference cannot be reasonably expanded, and the influence of learner historical registration course records on target courses is not considered. Different influences and related semantic information of courses provide a personalized learning resource recommendation method based on learner preference modeling

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  • Personalized learning resource recommendation method based on learner preference modeling
  • Personalized learning resource recommendation method based on learner preference modeling
  • Personalized learning resource recommendation method based on learner preference modeling

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Embodiment Construction

[0047]In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further elaborated below in combination with specific examples and with reference to the accompanying drawings.

[0048] The present invention uses the personalized learning resource recommendation based on learner preference modeling as an example to describe the specific implementation process of the method of the present invention. The model framework of the present invention is as figure 1 As shown, the overall process of personalized learning resource recommendation is as follows: figure 2 shown. Combined with the schematic diagram to illustrate the specific steps:

[0049] Step 1: Use crawler technology to collect historical record information of relevant learners’ registered courses from an online learning platform, such as obtaining courses learned by learners in chronological order (operational research→data mining→artificial intelli...

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Abstract

The invention provides a personalized learning resource recommendation method based on learner preference modeling. The method is characterized in that related learning log files of learners are obtained from an online learning platform; data such as historical course registration records, corresponding course scores and course related attributes of learners are taken as input data; historical course preferences of learners can be better obtained by embedding an attention mechanism; the coding input is used as the coding input of an automatic coder neural network, then a course knowledge graphis established to obtain a course pre-determined knowledge relationship, decoding is performed according to the correlation between courses, the probability of learning a target course by a learner is finally calculated, and a target recommendation list of the learner is generated according to the probability from large to small, so the individuation and accuracy of a recommendation result is improved.

Description

[0001] (1) Technical field [0002] The invention relates to technical fields such as machine learning, recommendation systems, and data mining, and in particular to a method for constructing learner portraits for learning resource recommendation. [0003] (2) Background technology [0004] With the in-depth development of education informatization, the number of online educational resources is showing an exponential growth trend. How to help learners obtain the learning resources they want from massive amounts of data is crucial for online learning platforms. Therefore, how to construct the learner's preference feature according to the learner's historical registration course is the key to the personalized learning resource recommendation system. Most of the previous learner preference modeling methods are based on the course records of the user's historical registration, and characterize the learner's preference characteristics by generating course feature vectors, and then g...

Claims

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Application Information

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IPC IPC(8): G06F16/951G06F16/9535G06N3/04G06N3/08G06Q50/20
CPCG06F16/951G06F16/9535G06Q50/205G06N3/08G06N3/045Y02D10/00
Inventor 刘铁园谭金丹常亮古天龙李龙
Owner GUILIN UNIV OF ELECTRONIC TECH
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