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Personalized learning remains a critical topic in the digital age, hence this paper seeks to investigate the potential benefits and capabilities of singular value decomposition (SVD) based collaborative filtering (CF) recommendation systems in delivering tailored educational support to learners. The discussion extends to how the synergy between big data and mobile devices is reshaping contemporary educational paradigms, with a focus on language learning. The technique of CF enables the provision of customized content suggestions based on individual learning behaviors and preferences. Further, the paper elaborates on the basic principles of Singular Value Decomposition and its effectiveness in processing large volumes of data, which includes enhancing the learning experience and boosting the precision of recommendation systems utilizing this technique. The paper also explores the implementation strategies and challenges of these systems on mobile platforms, corroborating the efficacy of the SVD based CF recommendation systems through empirical case studies. The research outcomes lay a theoretical foundation for creating more efficient tools for language learning and provide practical insights for leveraging big data and mobile technology to foster innovation in education.

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Personalized Language Learning Recommendation for Mobile Devices in the Context of Big Data: An SVD Based Collaborative Filtering Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Yanmei Zhao,
  • Yaqiong He,
  • Wenling Li,
  • Xuanyi Wu

摘要

Personalized learning remains a critical topic in the digital age, hence this paper seeks to investigate the potential benefits and capabilities of singular value decomposition (SVD) based collaborative filtering (CF) recommendation systems in delivering tailored educational support to learners. The discussion extends to how the synergy between big data and mobile devices is reshaping contemporary educational paradigms, with a focus on language learning. The technique of CF enables the provision of customized content suggestions based on individual learning behaviors and preferences. Further, the paper elaborates on the basic principles of Singular Value Decomposition and its effectiveness in processing large volumes of data, which includes enhancing the learning experience and boosting the precision of recommendation systems utilizing this technique. The paper also explores the implementation strategies and challenges of these systems on mobile platforms, corroborating the efficacy of the SVD based CF recommendation systems through empirical case studies. The research outcomes lay a theoretical foundation for creating more efficient tools for language learning and provide practical insights for leveraging big data and mobile technology to foster innovation in education.