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Digital education resource recommendation method and system

A technology for educational resources and recommendation methods, applied in the field of digital educational resource recommendation methods and systems, can solve the problems of difficult to obtain features, a large number of manual participation, etc., and achieve the effects of wide application, overcoming cold start, and high user satisfaction

Active Publication Date: 2016-08-17
HUAZHONG NORMAL UNIV
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AI Technical Summary

Problems solved by technology

This recommendation strategy first extracts the content characteristics of the recommended objects, and matches them with the user’s interests and preferences in the user model. The recommended objects with a high degree of matching can be recommended to users as recommendation results. However, building the content characteristics of resources often requires a lot of manual participation. , and it is difficult to obtain suitable features

Method used

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  • Digital education resource recommendation method and system
  • Digital education resource recommendation method and system
  • Digital education resource recommendation method and system

Examples

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example

[0062] The process of calculating student A’s interest in the learning resource "Introduction to Data Mining" is as follows: first obtain the text information of "Introduction to Data Mining", convert it into digital information, and obtain the text information M of "Introduction to Data Mining", Taking M as the input of the trained convolutional neural network, the predicted feature vector N of "Introduction to Data Mining" is obtained through the forward broadcast of the convolutional neural network, and then the feature vector S of student A is taken out from the student feature library, and the The vector N is multiplied by S to obtain the estimated value a of the student A's interest in the learning resource "Introduction to Data Mining". If a is greater than the threshold value preset by the system, it will be recommended, otherwise it will not be recommended.

[0063] In the example, the user-resource matrix is ​​shown in Table 1

[0064]

[0065] Among them, the vac...

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Abstract

The invention discloses a digital education resource recommendation method. The digital education resource recommendation method is characterized by comprising the first step of extracting text information of an education resource, the second step of digitalizing the text information to obtain a text digital matrix, the third step of inputting the text digital matrix into a resource feature recognizer obtained through pre-training and outputting an education resource feature vector through the resource feature recognizer, and the fourth step of multiplying the education resource feature vector by a user feature vector to obtain the interestingness of a student to the education resource. According to the method, education resource features are extracted from the resource text information, and by combining student features, the interestingness of the student to the resource is judged, the method is suitable for any resource as long as the resource has part of literal statements, the method is wide in application range, and the recommendation degree of satisfaction is high.

Description

technical field [0001] The invention relates to the field of educational informatization, in particular to a method and system for recommending digital educational resources. Background technique [0002] In the past decade, the rapid growth of Internet size and coverage has brought about the problem of information overload, to solve this problem recommendation system has become popular. Recommender systems are used in many scenarios, such as: movies, music, news, research papers, etc. In the field of online education based on the education cloud, a recommendation system is also adopted to enable students to improve learning efficiency and experience, and to provide students with personalized learning services. [0003] At present, there are many methods for implementing recommendation systems, and these methods can be mainly divided into two categories: collaborative filtering methods and content-based recommendation methods. The basic idea of ​​collaborative filtering is...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/9535G06F18/22
Inventor 刘海杨宗凯刘三女牙张昭理舒江波
Owner HUAZHONG NORMAL UNIV
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