Author(s)

Zhongbo Liu, Haixi Wang, Yutong Liu, Yilin Liu

Corresponding Author

Haixi Wang

Abstract

In today's era of data explosion and surging traffic, users often fall into the dilemma of "information loss" when facing massive amounts of information, especially when searching for personalized learning resources. With the booming development of online education platforms and the rapid growth of educational resources, how to efficiently and accurately recommend personalized learning resources to users has become a key issue that urgently needs to be solved. In response to this challenge, this article delves into the application of collaborative filtering (CF) algorithm in the field of educational resource recommendation, and designs a personalized recommendation system for educational resources based on this algorithm. The system constructs a complex network of relationships between users and resources by mining their historical learning behaviors, interests and preferences, as well as the inherent connections between educational resources, thereby achieving precise push of personalized learning resources. The experimental results show that the system can significantly shorten the time for users to search for learning resources, improve the accuracy of resource matching and user satisfaction, effectively solve the problem of students finding suitable content quickly in massive learning resources, and bring a more intelligent and personalized learning experience to the field of online education.

Keywords

Collaborative filtering algorithm, educational resources, personalized recommendation system