Development of a Teaching Model for Cultivating Applied Arts Talent in Universities in the Digital Media Era

This paper aims to revolutionize teaching and learning practices in higher education by optimizing prediction accuracy through recommendation system methods in applied art education. The study explores advanced machine learning techniques, particularly intuitive fuzzy C-means clustering and user interest collaborative filtering (IFCM-UIR-CF). Quantitative analysis reveals significant performance differences among three methods: FCM-CF, IFCM-CF, and IFCM-UIR-CF. FCM-CF starts with a Mean Absolute Error (MAE) of 0.845, improving to 0.750 with 150 neighbors. IFCM-CF starts at 0.810 and decreases to 0.700. IFCM-UIR-CF outperforms both, starting at 0.800 and reaching a minimum MAE of 0.658. These results show that increasing the number of nearest neighbors enhances prediction accuracy, with IFCM-UIR-CF providing the most accurate recommendations. The study emphasizes the significance of user-commodity relationships and decomposition processes in improving recommendation accuracy, offering insights for future research.