AI Predictive Music Streaming for Groups

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AI Predictive Music Streaming for Groups

Senior Design Showcase project, Wentworth Institute of Technology, College of Engineering and Computer Science, Department of Electrical and Computer Engineering, 2020

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title: AI Predictive Music Streaming for Groups

creator: Kristin Antone, Gabriela Bubalo, Tom Cleary

subject: Artificial intelligence, music, prediction, machine learning

description: Up to now, recommendation systems were tailored to a single user and it addresses the question of what “I like” as opposed to what “we like”. Given that music is a domain that is enjoyed both alone and, in a group, this recommendation perspective creates issues with using present streaming platform recommendations in a multi-user environment. When in a group environment, the most common objective is to displease the least and please the most people with music selection. With using current single-user tailored recommendation systems for group prediction, there is still a portion of the group left unsatisfied. Due to the complexity of pleasing multiple users at once, artificial intelligence principles will provide the recommended playlist, leaving no listener displeased. Recommendation systems today are built using Artificial Intelligence, which makes them adaptable to any changes, they are quick and provide precise recommendations for user’s preferences.

publisher: The Douglas D. Schumann Library and Learning Commons, Wentworth Institute of Technology

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date: 2020

type: Poster

format: .pdf

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language: eng

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rights: https://rightsstatements.org/vocab/InC/1.0/

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AI Predictive Music Streaming for Groups

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AI Predictive Music Streaming for Groups

AI Predictive Music Streaming for Groups

20.ECE.97 Poster