通过点处理镜头分析Spotify Top 200
Analyzing the Spotify Top 200 Through a Point Process Lens
摘要：
每一代人都会有一个英雄登上流行音乐排行榜。对于当代人来说，最相关的流行音乐排行榜之一是Spotify Top 200。Spotify是世界上最大的音乐流媒体服务，Top 200是该平台每天播放最多的200首歌曲。在本文中，我们分析了从这些排名中收集的20多个月的数据集。通过探索性的数据分析，我们调查的流行度，稀有性，和长寿的歌曲在前200名，我们构建了一个随机过程模型，每天流计数，借鉴的想法，从随机强度点过程和标记点过程。使用该模型的参数估计从前200名的数据，我们应用聚类算法来识别具有相似特征和性能的歌曲。
英文摘要：
Every generation throws a hero up the pop charts. For the current generation, one of the most relevant pop charts is the Spotify Top 200. Spotify is the world's largest music streaming service and the Top 200 is a daily list of the platform's 200 most streamed songs. In this paper, we analyze a data set collected from over 20 months of these rankings. Via exploratory data analysis, we investigate the popularity, rarity, and longevity of songs on the Top 200 and we construct a stochastic process model for the daily streaming counts that draws upon ideas from stochastic intensity point processes and marked point processes. Using the parameters of this model as estimated from the Top 200 data, we apply a clustering algorithm to identify songs with similar features and performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
