Research on Music Content Recognition and Recommendation Technology Based on Deep Learning

Abstract

With the development of information technology, various cloud music services are gradually emerging, which has fully changed and enriched people’s music life. How to propose the songs that consumers anticipate from the enormous song data is one of the key goals of the music recommendation system. This research aims to create a better music algorithm that incorporates user data for deep learning, a candidate matrix compression technique for suggestion improvement, accuracy, recall rate, and other metrics as evaluation criteria. In terms of recommendation methods, the music-music recommendation method based on predicting user behavior data and the recommendation method based on automatic tag generation are proposed. The music features obtained by audio processing are fully utilized, and the depth content information in music audio data is combined with other data for recommendation, which improves the tag quality and avoids the problem of low coverage. The results show that this model can extract the effective feature representation of songs in different classification criteria and achieve a good classification effect simultaneously.