Visualization of user interests in online music services

Online music services have been popular for end users to obtain music, where user interests, as reflected by their downloading records, are crucial for service providers to understand users and thus to provide personalization. However, the raw downloading records are of huge volume and difficult to analyze intuitively. We study a visualization approach to analyzing downloading records so as to present user interests. To reveal the underlying relevance between music tracks, we utilized not only the metadata of music (especially genres), but also collaborative relevance that is voted by users. To present time varying user interests, we designed several new figures, namely Bean plot, Instrument plot, and Transitional Pie plot, that are capable in displaying different aspects of user interests variation. We have performed experiments with a real-world data set, and the results show the effectiveness of our proposed visualization method. Our work is also inspiring for visualization of time varying data in other applications.