Learning resource recommendation method and system

Abstract

The invention provides a learning resource recommendation method, which comprises the following steps: collecting the learning information of a user; preprocessing the learning information to obtain effective learning information; analyzing the effective learning information to extract knowledge points; according to the effective learning information, obtaining the weight of each knowledge point; according to association strength among the knowledge points and the weights of the knowledge points, forming a knowledge point undigraph; and according to the weights of the knowledge points in the knowledge point undigraph, recommending learning resources to a user. The invention further provides a learning resource recommendation system, and the system independently establishes the knowledge point undigraph which conforms to own personality characteristics for different users and records and tracks the learning dynamics of the users in real time so as to improve learning resource recommendation accuracy and improve the learning efficiency and the experience degree of the user.

Description

Technical field [0001] The present invention relates to the field of electronic teaching, in particular to a method and system for recommending learning resources based on computer network learning. Background technique [0002] The traditional resource push method generally pushes the resource with the highest download rate to users. Although the download rate reflects the popularity of the resource to a certain extent, it cannot provide personalized recommendation to different users, especially In a learning system that includes many disciplines and professional knowledge points, users have different requirements for the content of learning resources. Even learning resources with a high click-through rate can only reflect the learning needs and preferences of a small number of users. [0003] Therefore, the existing learning resource push methods lack pertinence, and cannot push personalized learning resources according to the learning needs and interests of users, so that they c...

AI Technical Summary

Problems solved by technology

[0002] Traditional resource push methods generally push resources with the highest download rate to users. Although the download rate reflects the popularity of resources to a certain extent, it is impossible to make personalized recommendations for different users, especially in In a learning system that includes many disciplines and professional knowledge points, users have different needs for learning resources. Even learning resources with a high click-through rate can only reflect the learning needs and preferences of a small number of users.

[0003] Therefore, the existing methods for pushing learning resources lack pertinence, and cannot push personalized learning resources according to users' learning needs and interests, thereby failing to improve users' learning experience and stimulate users' learning interest, resulting in poor user experience.

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