Shashaani, S. (2024). Explainability in Music Recommender System. In T. Di Noia, P. Lops, T. Joachims, K. Verbert, P. Castells, Z. Dong, & B. London (Eds.), RecSys ’24: Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1395–1401). https://doi.org/10.1145/3640457.3688028
E194-04 - Forschungsbereich Data Science E180 - Fakultät für Informatik
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Published in:
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
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ISBN:
979-8-4007-0505-2
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Date (published):
2024
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Event name:
RecSys '24: 18th ACM Conference on Recommender Systems
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Event date:
14-Oct-2024 - 18-Oct-2024
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Event place:
Bari, Italy
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Number of Pages:
7
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Peer reviewed:
Yes
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Keywords:
Explainability; Explainable AI; Music Recommender System
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Abstract:
Recommendation systems play a crucial role in our daily lives, influencing many of our significant and minor decisions. These systems also have become integral to the music industry, guiding users to discover new content based on their tastes. However, the lack of transparency in these systems often leaves users questioning the rationale behind recommendations. To address this issue, adding transparency and explainability to recommender systems is a promising solution. Enhancing the explainability of these systems can significantly improve user trust and satisfaction. This research focuses on exploring transparency and explainability in the context of recommendation systems, focusing on the music domain. This research can help to understand the gaps in explainability in music recommender systems to create more engaging and trustworthy music recommendations.
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Project title:
Empfehlungssystem & Nutzer: Hin zu gegenseitigem Verständnis: P 33526-N (FWF - Österr. Wissenschaftsfonds)
