| Title |
Research on Intelligent Educational Content Recommendation System Integrating Deep Learning and Natural Language Processing |
| Authors |
(Wenyan Zhao) ; (Xiaowen Zhang) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.5.683 |
| Keywords |
Deep learning; Natural language processing; Fusion method; Intelligent education; Recommendation system |
| Abstract |
Currently, research on intelligent educational content recommendation systems suffers from issues like inadequate content comprehension and inaccurate personalized recommendations, primarily due to the lack of effective content understanding and user behavior analysis techniques. This study innovatively integrates deep learning and natural language processing to address these challenges. Deep learning models enhance text content analysis, while natural language processing enables precise exploration of user needs. Leveraging a vast dataset encompassing students’ online learning trajectories and educational text information, the proposed system combines deep neural networks with natural language processing to dissect text semantics and structures, identify learners’ interests and knowledge deficiencies, and rapidly retrieve optimal learning materials from millions of educational resources.
Empirical results demonstrate significant improvements: the AUC value, which gauges the system’s ability to differentiate suitable from unsuitable resources, increases by 23%, and the click-through rate of recommendation lists rises by approximately 19% compared to traditional collaborative filtering methods. The system-generated recommendations better align with users’ actual requirements, boosting learning efficiency and satisfaction, and enhancing the accuracy and personalization of intelligent educational content recommendation systems |