| Title |
Research on the Application of Course Recommendation Algorithms in English Education Platforms Based on Big Data Analysis |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.5.656 |
| Keywords |
Big data environment; Recommendation algorithm; FGBLFM algorithm; English education platform |
| Abstract |
The English education platform model recommendation algorithm stands out as a pivotal tool in the realm of Internet information services owing to its remarkable adaptability within the realm of big data. Through a meticulous examination of various recommendation algorithms, including the mainstream ones, and specifically targeting the English education platform model algorithm, groundbreaking advancements have been achieved. Amidst intricate challenges of training in vast data environments due to an excess of parameters, an innovative approach has been proposed. Enter the FGBLFM algorithm?a refined version based on function space gradient enhancement.
This algorithm outshines traditional models by offering superior insights into user-item relationships and enhancing the accuracy of inference ratings within English education platforms. Noteworthy improvements have been made in spelling error detection through the integration of named entity recognition. Identification accuracy has surged, reducing errors in recognizing proper nouns by a considerable margin. Furthermore, error correction suggestions saw a significant accuracy boost, showcasing enhancements from 0.862 to 0.941?a substantial 9.16% rise in accuracy. Delving deeper, the FGBLFM algorithm has been successfully implemented on the Spark distributed processing platform. By utilizing the Movie Lens dataset for evaluation with RMSE and MAE metrics, comparisons against collaborative filtering methods and existing English education platform models unveiled the algorithm’s effectiveness. Through this holistic approach, the FGBLFM algorithm has solidified its position as a game-changer in the landscape of recommendation algorithms, marking a paradigm shift in driving heightened accuracy, enhanced performance, and usability within English education platforms. |