My friends also prefer diverse music: homophily and link prediction with user preferences for mainstream, novelty, and diversity in music

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung


Homophily describes the phenomenon that similarity breeds connection, i.e., individuals tend to form ties with other people who are similar to themselves in some aspect(s). The similarity in music taste can undoubtedly influence who we make friends with and shape our social circles. In this paper, we study homophily in an online music platform regarding user preferences towards listening to mainstream (M), novel (N), or diverse (D) content. Furthermore, we draw comparisons with homophily based on listening profiles derived from artists users have listened to in the past, i.e., artist profiles. Finally, we explore the utility of users' artist profiles as well as features describing M, N, and D for the task of link prediction. Our study reveals that: (i) users with a friendship connection share similar music taste based on their artist profiles; (ii) on average, a measure of how diverse is the music two users listen to is a stronger predictor of friendship than measures of their preferences towards mainstream or novel content, i.e., homophily is stronger for D than for M and N; (iii) some user groups such as high-novelty-seekers (explorers) exhibit strong homophily, but lower than average artist profile similarity; (iv) using M, N and D achieves comparable results on link prediction accuracy compared with using artist profiles, but the combination of features yields the best accuracy results, and (v) using combined features does not add value if graph-based features such as common neighbors are available, making M, N, and D features primarily useful in a cold-start user recommendation setting for users with few friendship connections. The insights from this study will inform future work on social context-aware music recommendation, user modeling, and link prediction.
Titel2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2021)
ISBN (elektronisch)978-1-4503-9128-3
PublikationsstatusAngenommen/In Druck - 31 Okt 2021
Veranstaltung2021 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining: ASONAM 2021
- Virtuell, Niederlande
Dauer: 8 Nov 202111 Nov 2021


Konferenz2021 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining
KurztitelASONAM 2021

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