Aliasghar Erteghaeian
Fairness in Music Recommendation Systems.
Rel. Cristina Emma Margherita Rottondi, Massimiliano Zanoni. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2026
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview |
|
|
Archive (ZIP) (Documenti_allegati)
- Altro
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (4MB) |
Abstract
The advent of online streaming services has revolutionized the entertainment consumption, in more ways than one. The availability of thousands of hours of entertainment, however, came with caveats, as it created an overwhelming experience for the user to navigate through and discover the most relevant content. Thus, the significant shift from analog to digital has made the presence of recommender algorithms an urgent aspect for the modern media landscape. Online entertainment platforms were forced to adopt automated mechanisms to offer customized curation to users, in the most efficient manner. From online TV and movie providers such as Netflix and Amazon Prime to video sharing services such as YouTube, to online music services on world-leading platforms such as Spotify and Apple Music, the use of recommender tools has become an industry standard.
Since the recommendation methodologies can vary widely across different entertainment sectors, this study aims specifically at the recommendation practice in a music streaming service
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modifica (riservato agli operatori) |
