Design and application of a music recommendation system based on user behavior and feature recognition

Currently, music recommendation systems have significant limitations in user behavior analysis, resulting in lower accuracy in recommendations. To address these issues, we propose a music recommendation system based on user behavior and feature recognition, leveraging deep learning for training user data. User behavior sequences are inputted into an encoder to obtain datasets, detecting user preferences based on weight values. User gradients are derived through weighted partitioning, extracting user behavior intentions. User interest is statistically assessed based on the time spent listening to music, calculating a personalized music information matrix. Subset relevance is compared to achieve music information recommendations. Experimental comparisons with traditional systems show that the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) fluctuate between 0 and 1, with recommendation accuracy exceeding 87.5 % and peaking at 99 %, indicating excellent recommendation performance.