Spotify Playlist Reordering Website ; New Social Media Impact on Aging Society in China

Wang, Yifan, School of Engineering and Applied Science, University of Virginia
Praphamontripong, Upsorn, EN-Comp Science Dept, University of Virginia
Ku, Tsai-Hsuan, EN-Engineering and Society, University of Virginia

With over 200 million monthly active users, Spotify is one of the best streaming music services nowadays. However, Spotify lacks the functionality of rearranging users’ playlists based on users’ needs. In the technical paper, we first introduces several music datasets we found that are suitable for related studies, then we will explore different machine learning models fitting the datasets and playlists. We will then rearrange and generate the list according to the models and make comparisons between the results in order to find optimal and viable approaches. Finally we host the approaches we designed on a website and allow Spotify users to login and use.

The technical subject of the STS prospectus and the technical topic for the Dept. of Computer Science is not closely related. Nevertheless, both of the topics are related to the new media. My STS prospectus looks into the sociological impact of social media on the seniority in China. Social media is altering the lifestyles of the retired significantly, and the research is conducted on age groups above 50 aiming to have a better understanding of why traditional media including newspaper and television is fading out of the daily life of the elderly and is gradually being substituted by the new social media such as WeChat. Similar to Spotify streaming music services which has an exponential growth of user numbers over the past decade, WeChat, a multi-purpose messaging, social media and mobile payment app, first released in 2011 has become the largest mobile app in China.

BS (Bachelor of Science)
Spotify playlist reordering, Audio features machine learning models traning, New social media, Chinese aging society

School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Upsorn Praphamontripong
STS Advisor: Tsai-Hsuan Ku
Technical Team Members: Isaac Li, Leo Wang

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