Deep learning-based intelligent recommendation system for French language teaching

With the close integration of information technology and education, new online education has become an indispensable part of smart education. It breaks the limitations of time and space and builds a flexible and resource rich online learning platform for learners. In this context, French learners also benefit from this change and enjoy unprecedented learning freedom. However, an obvious problem is how to select content that meets individual needs and effectively enhances French skills from such a vast library of learning resources, which has become an urgent challenge for many learners. To address this challenge, this paper proposes a French resource intelligent recommendation system based on deep learning (DL). This system uses DL technology to deeply explore diverse information such as learners' behavior data, learning progress, and interest preferences, in order to construct personalized learning models. Experimental data shows that the system can accurately capture learners' needs, intelligently select the most relevant and effective learning materials from massive resources, and significantly improve learning efficiency and experience.