Personalized Analysis and Recommendation of Aesthetic Evaluation Index of Dance Music Based on Intelligent Algorithm

Abstract

In the era of Industry 4.0 and 5G, various dance music websites provide thousands of dances and songs, which meet people's needs for dance music and bring great convenience to people. However, the rapid development of dance music has caused the overload of dance music information. Faced with a large number of dances and songs, it is difficult for people to quickly find dance music that conforms to their own interests. The emergence of dance music recommendation system can recommend dance music that users may like and help users quickly discover or find their favorite dances and songs. This kind of recommendation service can provide users with a good experience and bring commercial benefits, so the field of dance music recommendation has become the research direction of industry and scholars. According to different groups of individual aesthetic standards of dance music, this paper introduces the idea of relation learning into dance music recommendation system and applies the relation model to dance music recommendation. In the experiment, the accuracy and recall rate are used to verify the effectiveness of the model in the direction of dance music recommendation.