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Course recommendation method, device, electronic equipment and medium based on artificial intelligence

A technology of artificial intelligence and recommendation methods, which is applied in the fields of electrical digital data processing, instruments, data processing applications, etc., can solve the problems of inaccurate course recommendation and poor course recommendation effect, and achieve the effect of solving the cold start problem

Active Publication Date: 2021-12-10
PING AN TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although these methods have alleviated the cold start problem of users to a certain extent, they are relatively subjective and one-sided, and require specific domain knowledge and more user interaction, resulting in poor course recommendation effect for cold start users, and cannot be accurate. Make course recommendations

Method used

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  • Course recommendation method, device, electronic equipment and medium based on artificial intelligence
  • Course recommendation method, device, electronic equipment and medium based on artificial intelligence
  • Course recommendation method, device, electronic equipment and medium based on artificial intelligence

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0059] figure 1 It is a flow chart of the artificial intelligence-based course recommendation method provided in Embodiment 1 of the present invention. The artificial intelligence-based course recommendation method specifically includes the following steps. According to different requirements, the order of the steps in the flow chart can be changed, and some of them can be omitted.

[0060] S11, generating a user information matrix based on the information reading behavior of the first user in the information field, and generating a user course matrix based on the course learning behavior of the second user in the course field.

[0061] Wherein, the course field refers to a field for training course recommendation, and the information field refers to a field related to the course field. The course domain is used as the target domain, and the information domain is used as the source domain. The users in the information field are the first users, and the users in the course fi...

Embodiment 2

[0126] figure 2 It is a structural diagram of an artificial intelligence-based course recommendation device provided in Embodiment 2 of the present invention.

[0127] In some embodiments, the artificial intelligence-based course recommendation device 20 may include a plurality of functional modules composed of computer program segments. The computer program of each program segment in the artificial intelligence-based course recommendation device 20 can be stored in the memory of the electronic device, and executed by at least one processor to execute (see for details figure 1 Description) Features of artificial intelligence-based course recommendation.

[0128] In this embodiment, the artificial intelligence-based course recommendation device 20 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: a matrix generation module 201 , an information calculation module 202 , a first building module 203 , a us...

Embodiment 3

[0195] This embodiment provides a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in the above-mentioned embodiment of the artificial intelligence-based course recommendation method are implemented, for example figure 1 S11-S16 shown:

[0196] S11, generating a user information matrix based on the reading behavior of the first user in the information field, and generating a user course matrix based on the learning behavior of the second user in the course field;

[0197] S12. Calculate the first similarity of a first user pair formed by any two first users in the information field according to the user information matrix;

[0198] S13. Construct a first user domain set in the information domain according to the first similarity;

[0199] S14. For any first user pair in the first user field set, acquire a second user pair matching the any first user pair ...

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Abstract

The present invention relates to the field of artificial intelligence technology, and provides an artificial intelligence-based course recommendation method, device, electronic equipment, and medium. After generating a user information matrix in the information field and a user course matrix in the course field, the information field is calculated through the user information matrix According to the similarity between any two users, the first user domain set is constructed according to the similarity, so that the second user domain set of the course domain is constructed according to the user course matrix and the first user domain set, and the transfer learning idea is used to transfer the user The preference behavior of reading information is migrated to the preference behavior of learning courses in the course domain, and finally recommends courses for two similar second users in the second user domain set, effectively solving the cold start in the course recommendation model The problem is that it can not only recommend personalized courses for multiple users at one time, but also realize the mutual recommendation of courses for two similar users.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based course recommendation method, device, electronic equipment and medium. Background technique [0002] The course recommendation model usually uses a large amount of user historical behavior data as input, and at the same time uses other auxiliary information to predict the user's preference for a certain course, so as to achieve accurate push of differentiated training courses. However, the course recommendation model often faces the problem of user cold start, that is, when there is a lack of historical interaction data between users and courses, the course recommendation model cannot judge the user's demand preference for courses, and thus cannot implement targeted course recommendations. [0003] The inventor found in the process of implementing the present invention that most of the existing methods for solving the user ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06F16/9536G06F17/16G06K9/62G06Q50/20
CPCG06F16/9535G06F16/9536G06F17/16G06Q50/205G06F18/22
Inventor 杨德杰张茜叶聆音许丹
Owner PING AN TECH (SHENZHEN) CO LTD
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