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Design and Implementation of Online Learning Platform for College Students based on Collaborative Filtering Algorithm

Author(s)

Zihao Qian

Affiliation(s)

College of Computer Science, Guangzhou Institute of Applied Technology, Guangzhou, Guangdong, China

Abstract

To address the dual challenges of "information overload" and "insufficient fulfillment of personalized learning needs" in current online education platforms, this study designs and implements a college student learning platform featuring precise course recommendation capabilities. The system employs a collaborative filtering algorithm as its core recommendation engine, which calculates Pearson correlation coefficients between users to measure interest similarity, thereby generating customized course recommendation lists for target learners. Developed using the Spring Boot and Vue3 front-end/back-end architecture, preliminary functional testing and user evaluations demonstrate that the platform significantly enhances both the relevance of course recommendations and user satisfaction. This research provides a practical technical solution for building intelligent online learning environments while exploring challenges such as cold start issues in applying collaborative filtering algorithms within educational contexts.

Keywords

Collaborative Filtering; Online Learning Platform; Recommendation Algorithm; Pearson Correlation Coefficient

References

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