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Title: The Diversity of Music Recommender Systems
While the algorithms used by music streaming services to provide recommendations have often been studied in offline, isolated settings, little research has been conducted studying the nature of their recommendations within the full context of the system itself. This work seeks to compare the level of diversity of the real-world recommendations provided by five of the most popular music streaming services, given the same lists of low-, medium- and high-diversity input items. We contextualized our results by examining the reviews for each of the five services on the Google Play Store, focusing on users’ perception of their recommender systems and the diversity of their output. We found that YouTube Music offered the most diverse recommendations, but the perception of the recommenders was similar across the five services. Consumers had multiple perspectives on the recommendations provided by their music service—ranging from not wanting any recommendations to applauding the algorithm for helping them find new music.  more » « less
Award ID(s):
2045153
PAR ID:
10402477
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
IUI '22 Companion: 27th International Conference on Intelligent User Interfaces
Page Range / eLocation ID:
97 to 100
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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