Construction and Operation management of electronic information education learning community-based on collaborative filtering algorithm

Abstract

This study investigates the construction and operation management of an electronic information education learning community utilizing collaborative filtering algorithms. Collaborative filtering techniques, renowned for their effectiveness in recommendation systems, are employed to personalize learning experiences within the digital education landscape. Through meticulous data collection and algorithmic implementation, the study demonstrates the algorithm's ability to predict user preferences for educational content accurately. Precision, recall, and F1 score metrics highlight the algorithm's efficacy in tailoring recommendations to individual learners' needs. Furthermore, the study examines the impact of personalized recommendations on user engagement and learning outcomes. Significant increases in click-through rates, time spent on the platform, and participation rates underscore the positive influence of personalized recommendations on user interaction and knowledge acquisition. These findings suggest that collaborative filtering algorithms hold immense potential in optimizing digital learning platforms by creating adaptive, user-centric environments. While acknowledging limitations and advocating for future research, this study contributes to advancing the discourse on leveraging technology to enhance educational experiences, fostering a culture of lifelong learning and knowledge sharing in the digital era.