The global rise of K-pop and the digital revolution have paved the way for new dimensions in artist recommendations. With platforms like Twitter serving as a hub for fans to interact, share and discuss K-pop, a vast amount of data is generated that can be analyzed to understand listener preferences. However, current recommendation systems often overlook K- pop's inherent diversity, treating it as a singular entity. This paper presents an innovative method that utilizes Natural Language Processing to analyze tweet content and discern individual listening habits and preferences. The mass of Twitter data is methodically categorized using fan clusters, facilitating granular and personalized artist recommendations. Our approach marries the advanced GPT-4 model with large-scale social media data, offering potential enhancements in accuracy for K-pop recommendation systems and promising an elevated, personalized fan experience. In conclusion, acknowledging the heterogeneity within fanbases and capitalizing on readily available social media data marks a significant stride towards advancing personalized music recommendation systems.

翻译：K-pop的全球兴起与数字革命为艺人推荐开辟了新的维度。随着Twitter等平台成为粉丝互动、分享和讨论K-pop的中心，产生了大量可用于分析听众偏好的数据。然而，当前的推荐系统常忽视K-pop固有的多样性，将其视为单一实体。本文提出一种创新方法，利用自然语言处理技术分析推文内容，以识别个体听歌习惯与偏好。通过粉丝聚类对海量Twitter数据进行系统分类，从而实现细粒度、个性化的艺人推荐。我们的方法将先进的GPT-4模型与大规模社交媒体数据相结合，有望提升K-pop推荐系统的准确性，并为粉丝带来更优质的个性化体验。总之，认识粉丝群体的异质性并充分利用现成的社交媒体数据，标志着个性化音乐推荐系统发展的重要进步。