Music streaming providers closely rely on recommender programs to assist customers discover and enjoy new musical content material within giant catalogs of millions of songs, artists and albums, with the overall goal of improving their experience and engagement (Briand et al., 2021; Schedl et al., 2018; Bendada et al., 2020; Mehrotra et al., 2019). Specifically, these providers continuously recommend, on an artist’s profile page, a ranked listing of associated artists that fans also listened to or appreciated (Donker, 2019; Johnston, 2019; Kjus, 2016). Referred to as ”Fans Additionally Like” on Spotify and Soundcloud and as ”Related” or ”Similar Artists” on Amazon Music, Apple Music and Deezer, such a function sometimes leverages learning to rank fashions (Karatzoglou et al., 2013; Schedl et al., 2018; Rafailidis and Crestani, 2017). It retrieves probably the most related artists in line with similarity measures often computed from utilization knowledge, e.g. from the proportion of shared listeners throughout artists (Donker, 2019; Johnston, 2019), or from more advanced collaborative filtering models (Jain et al., 2020; Koren and Bell, 2015; Schedl et al., 2018) that predict similarities from the known preferences of an artist’s listeners.

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