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This paper aims to address the issue of personalized content recommendation in remote education platforms, with the goal of enhancing the learning experience through precise management of the educational database. The article begins by introducing web personalization techniques, including content, rule-based, and collaborative filtering algorithms for recommendation. Subsequently, the paper expands on personalized recommendation algorithms tailored to specific needs in remote education, providing a detailed description of how these algorithms utilize user behavior analysis to accurately capture user interests and deliver educational content accordingly. The paper thoroughly describes the design and implementation process of the data collection system, encompassing the technical framework for collecting crucial user learning behavior data, as well as the development environment implemented using embedded Linux and SQLite ODBC interface. Furthermore, comprehensive testing is conducted to ensure the performance and stability of the data management system in high concurrent learning environments. Overall, the research findings not only contribute to the design of efficient database management systems to support remote education recommendation algorithms, but also offer valuable insights for enhancing learner interactivity and personalized experiences.
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