Social music discovery: an ethical recommendation system based on friend's preferred songs.
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| Title: | Social music discovery: an ethical recommendation system based on friend's preferred songs. |
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| Authors: | Furini, Marco1 (AUTHOR) marco.furini@unimore.it, Fragnelli, Francesca2 (AUTHOR) |
| Source: | Multimedia Tools & Applications. Apr2025, Vol. 84 Issue 14, p13469-13483. 15p. |
| Subjects: | Recommender systems, Multiple Signal Classification, Musical aesthetics, Client satisfaction, Satisfaction, Musical perception |
| Abstract: | Music recommendation systems have become ubiquitous in today's world, but they raise ethical concerns related to bias, discrimination, and lack of transparency. To address these issues, we propose a recommendation system that combines content-based and collaborative filtering approaches within three different recommendation algorithms. These algorithms create playlists that mimic the user's listening habits while identifying similar tracks within the listening histories of the user's friends. To evaluate the effectiveness of our system, we asked ten participants to rate a total of ninety playlists. The results showed high satisfaction among participants with the playlists generated by two of the proposed recommendation algorithms. Specifically, participants who preferred to stay within their musical comfort zone appreciated one specific recommendation algorithm, while those who were willing to explore new music tended appreciated the other recommendation algorithm. In summary, by leveraging the user's social connections, our proposed system provides a more transparent and ethical approach to music recommendations. It provides a personalized and enjoyable music discovery experience that considers the nuances of individual musical taste and preferences. These findings suggest the potential impact of our proposal in addressing ethical concerns and enhancing user satisfaction in music recommendation services. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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