Integration of Music Database System based on Big Data and Mobile Internet Technology

The rapid growth of music data and diverse user demands have challenged traditional music database systems in processing efficiency and personalized recommendations. To address this, we propose an integrated system leveraging Hadoop + Spark for big data processing and Neural Collaborative Filtering (NCF) for recommendations. The Hadoop framework enables distributed storage (HDFS) and batch/real-time data processing via Spark, while NCF combines matrix factorization and deep learning to model user-item interactions. Experiments evaluated four aspects: data processing efficiency, retrieval speed, user satisfaction, and recommendation accuracy. Results showed Hadoop+Spark processed 100TB of data in 7.5 hours outperforming Apache Flink and Google BigQuery. Retrieval speeds averaged 0.8 seconds (100k songs) and 2.3 seconds (1M songs), surpassing benchmarks. The NCF algorithm achieved 80% accuracy and 75% recall, surpassing matrix factorization and collaborative filtering. This study demonstrates that integrating Hadoop+Spark with NCF significantly enhances processing efficiency, recommendation precision, and user experience. Future work will focus on dynamic model optimization and expanding multimodal data integration.