Abstract
This chapter gives an introduction to music recommender systems, considering the unique characteristics of the music domain. We take a user-centric perspective, by organizing our discussion with respect to current use cases and challenges. More precisely, we categorize music recommendation tasks into three major types of use cases: basic music recommendation, lean-in exploration, and lean-back listening. Subsequently, we explain the main categories of music recommender systems from a technical perspective, including content-based filtering, sequential recommendation, and recent psychology-inspired approaches. To round off the chapter, we provide a discussion of challenges faced in music recommendation research and practice, and of approaches that address these challenges. Topics we address here include creating multi-faceted recommendation lists, considering intrinsic user characteristics, making fair recommendations, explaining recommendations, evaluation, dealing with missing and negative feedback, designing user interfaces, and providing open tools and data sources.
Citation
Markus
Schedl,
Peter Knees,
Brian McFee,
Dmitry Bogdanov
Music Recommendation Systems: Techniques, Use Cases, and Challenges
Recommender Systems Handbook (3rd edition),
927--971, doi:10.1007/978-1-0716-2197-4_24, 2022.
BibTeX
@incollection{Schedl2022Music,
title = {Music Recommendation Systems: Techniques, Use Cases, and Challenges},
author = {Schedl, Markus and Knees, Peter and McFee, Brian and Bogdanov, Dmitry},
booktitle = {Recommender Systems Handbook (3rd edition)},
editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha},
publisher = {Springer},
address = {New York, NY},
doi = {10.1007/978-1-0716-2197-4_24},
url = {https://doi.org/10.1007/978-1-0716-2197-4_24},
isbn = {978-1-0716-2197-4},
pages = {927--971},
year = {2022}
}