A collaborative filtering recommendation method and system for learning resources based on knowledge association

Abstract

The invention belongs to the field of personalized intelligent recommendation, a collaborative filtering recommendation method and system for learning resources based on knowledge association are disclosed. By combining with the relationship between knowledge and the relationship between resources and knowledge points, the information of knowledge points associated with learners is introduced intothe methods of user similarity calculation and interest calculation, and then the nearest neighbor user group of target learners is obtained, and learner-Learning resource interest score matrix is constructed. Then, the interest score of the resources that the current users are not yet learning and may be interested in is predicted by the knowledge points preference of the similar user groups. Finally, N results with higher interest score are recommended to the current learners. The invention designs a collaborative filtering recommendation algorithm of learning resources based on knowledge association according to the relationship among learners, knowledge points and learning resources, so that the recommendation result is more in line with the actual learning needs of learners.

Description

technical field [0001] The invention belongs to the field of personalized intelligent recommendation, and in particular relates to a learning resource collaborative filtering recommendation method and system based on knowledge association. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: [0003] Today, with the rapid development of information technology, facing the massive learning resources on the Internet, learners often face the problem of "resource loss", and a personalized intelligent recommendation system is urgently needed to improve learning efficiency. Collaborative filtering uses group wisdom to provide users with recommendation services, and has rapidly grown into the most widely used technology in personalized recommendation systems. [0004] For example, Yang YJ et al. designed the AACS learning resource recommendation system using ant colony algorithm and Kolb learning style model. The system p...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the problems of inaccurate calculation of user similarity, low accuracy of interest score prediction and low recommendation quality in the existing collaborative filtering recommendation system of learning resources due to "cold start" and "data sparseness"

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