MuRS 2024: 2nd Music Recommender Systems Workshop
Pages 1202 - 1205
Abstract
Music recommendation has been relevant to the Recommender Systems (RecSys) community since the early days. With the growth of music streaming platforms, algorithmic recommendations have become critical in the music industry. However, many challenges are still wide open in the area of music recommender systems. Such challenges are currently being addressed in several research communities, including and beyond the RecSys and the Music Information Retrieval (MIR) communities. The RecSys conference has traditionally not focused very much on music content understanding. In contrast, while music content understanding is central to the MIR community, research on recommender systems is not prominent in MIR research. The Music Recommender Systems Workshop (MuRS) aims at bridging the existing gap between the diverse research communities focused on the specific challenges of music recommender systems. The workshop provides a space for researchers and practitioners from multiple disciplines to jointly discuss and exchange perspectives and solutions, and to promote discussion from both academia and industry upon future research directions in the area of music recommender systems.
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Index Terms
- MuRS 2024: 2nd Music Recommender Systems Workshop
Recommendations
Machine Learning Approaches to Hybrid Music Recommender Systems
Machine Learning and Knowledge Discovery in DatabasesAbstractMusic recommender systems have become a key technology supporting the access to increasingly larger music catalogs in on-line music streaming services, on-line music shops, and private collections. The interaction of users with large music ...
Music4All-Onion -- A Large-Scale Multi-faceted Content-Centric Music Recommendation Dataset
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementWhen we appreciate a piece of music, it is most naturally because of its content, including rhythmic, tonal, and timbral elements as well as its lyrics and semantics. This suggests that the human affinity for music is inherently content-driven. This ...
MuRS: Music Recommender Systems Workshop
RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems
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Published In
October 2024
1438 pages
ISBN:9798400705052
DOI:10.1145/3640457
Copyright © 2024 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 October 2024
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- Refereed limited
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- Austrian Science Fund
- Land Salzburg
- Joint Research Centre
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Overall Acceptance Rate 254 of 1,295 submissions, 20%
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