<![CDATA[Music recommendation systems help users navigate large music collections by suggesting songs aligned with their preferences. However, conventional methods often overlook the depth of audio content, limiting personalization and accuracy. This study proposes a hybrid approach that uses PCA and Autoencoder to extract audio embeddings. These embeddings are processed using K-Nearest Neighbors to find similar tracks, followed by a reranking step with LightGBM based on predicted relevance. The system achieved strong results: 98% accuracy, 0.96 precision, 0.96 recall, and 0.96 F1-score for the Similar class, with 0.99 precision and recall for Not Similar. Cross-validation confirmed model robustness, with an average accuracy of 97.99%, precision of 0.9577, recall of 0.9624, and F1-score of 0.9600, all with low standard deviations. These outcomes show that combining deep audio features with machine learning ranking enhances recommendation quality. Future improvements may involve incorporating metadata and genre-based visualizations for more diverse and interpretable results.]]>
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