Please note: This master’s thesis presentation will be given online.
Kyle Robinson, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Dan Brown
While many researchers have proposed various ways of quantifying recommendation list diversity, these approaches have had little input from users on their own perceptions and preferences in seeking diversity. Through a set of user studies we provide a better understanding of how users view the concept of diversity in music recommendations, and how intra-list diversity can be adapted to better represent their diversity preference. Our results show that users have a clear idea of what music recommendation diversity means to them, accuracy metrics do not model overall list satisfaction, and filtering recommendations on genre before list diversification can positively impact list satisfaction. More importantly, our results highlight the need to base music recommendation metrics on insights from real people.
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