Design of music recommendation system based on EDA and K-means cluster analysis

This paper describes the construction and analysis of a music recommendation system, through visual and exploratory analysis of the open source dataset of music recommendation system in the UCL database, exploring the relationship between music popularity and music acoustic properties, using the K-means clustering algorithm for clustering analysis of genres and songs, and finally building a music recommendation system that can, based on the user input of music data, to match the ten music songs that best meet the listener's taste. The analyses in this paper show that there is a certain correlation between music popularity and the indicators of sound, danceability, energy, instrumentality, liveliness, loudness, language, rhythm, mood, duration and tone. The indicators of energy, liveliness, loudness and tempo are positively correlated with the popularity of music, while the indicators of mood and duration are negatively correlated with the popularity of music. In addition, this paper uses K-means clustering algorithm to cluster analysis of genres and songs, and shows the similarity and difference between different genres and songs through visualisation, which provides important data support for the construction of music recommendation system. Finally, this paper establishes a music recommendation system based on K-means clustering algorithm, which can match the ten music songs that best meet the listener's taste according to the music data input by the user. The system is intelligent and personalised, and can recommend music works that are more in line with the user's taste based on the user's preferences and historical listening records. The results of this paper provide useful reference and support for the algorithms and models of the music recommendation system, which can be further researched in depth in the future to improve its intelligence and personalisation level and provide users with better music services.