Personalized learning resource recommendation system and method under E-learning platform

Abstract

The invention provides a personalized learning resource recommendation system and method under an E-learning platform.The system mainly comprises a management module mainly used for managing user information and learning resources, a recommendation strategy module used for recommending the learning resources for users logging into a learning platform, and a database module used for storing various required basic data including information tables.The method includes the steps that the users log into a learning system to learn and grade the learning resources; the learning resources which the users may be interested in are recommended for the users according to the optimized collaborative filtering recommendation algorithm.The optimized method solves the problem that in a traditional collaborative filtering algorithm, because of grade matrix collaborative filtering, the similarity calculation is not accurate.In addition, user interest drifting is considered, time factors are introduced, and finally the personalized learning resource recommendation efficiency and accuracy are improved.

Description

technical field [0001] The invention belongs to the field of data mining under the E-learning platform, and in particular relates to a learning resource personalized recommendation system and method under the E-learning platform. Background technique [0002] With the development of the Internet, learning activities have expanded from the classroom to the Internet, and many E-learning platforms have emerged. However, there are still many defects in the current online learning system: the utilization rate of teaching resources is not high; there are countless learning resources, and learners are looking for needles in a haystack. Generally, it is impossible to quickly find the required resources; learners need to manually input description words to search, and the system cannot actively recommend based on the user's own information. These defects make e-learning lose its original advantages. Therefore, it is urgent to integrate personalized service into E-learning platform. ...

AI Technical Summary

Problems solved by technology

[0002] With the development of the Internet, learning activities have expanded from the classroom to the Internet, and many E-learning platforms have emerged. However, there are still many defects in the current online learning system: the utilization rate of teaching resources is not high; there are countless learning resources, and learners are like looking for a needle in a haystack. Generally, it is impossible to quickly find the required resources; learners need to manually enter the description vocabulary to search, and the system cannot actively recommend based on the user's own information

[0003] The application of personalized recommendation technology in the field of e-commerce is very successful, but the personalized recommendation applied to the learning platform is still more theoretical than practical. The collaborative filtering recommendation algorithm will not be limited by the complexity and diversity of recommended resources, but due to collaborative filtering The data sparsity problem in the recommendation algorithm will largely lead to the inaccuracy of the recommended resources

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