review-article

Content-driven music recommendation: : Evolution, state of the art, and challenges

Authors:

Yashar Deldjoo,

Peter KneesAuthors Info & Claims

Volume 51, Issue C

https://doi.org/10.1016/j.cosrev.2024.100618

Published: 25 June 2024 Publication History

Abstract

The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music recommenders have traditionally embraced content-based (CB) approaches. In the past years, music recommendation models that leverage collaborative and content data – which we refer to as content-driven models – have been replacing pure CF or CB models. In this survey, we review 55 articles on content-driven music recommendation. Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, user-generated content, and derivative content. We provide a detailed characterization of each category along several dimensions. Second, we identify six overarching challenges, according to which we organize our main discussion: increasing recommendation diversity and novelty, providing transparency and explanations, accomplishing context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start. Each article addresses one or more of these challenges and is categorized according to the content layers of our onion model, the article’s goal(s), and main methodological choices. Furthermore, articles are discussed in temporal order to shed light on the evolution of content-driven music recommendation strategies. Finally, we provide our personal selection of the persisting grand challenges which are still waiting to be solved in future research endeavors.

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Cited By

Zeng ZWang YZhao YShi W(2024)A Survey of Music Recommendation SystemsProceedings of the 5th International Conference on Computer Information and Big Data Applications10.1145/3671151.3671243(507-519)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3671151.3671243
Martinez ATufis MBoratto LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Unmasking Privacy: A Reproduction and Evaluation Study of Obfuscation-based Perturbation Techniques for Collaborative FilteringProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657858(1753-1762)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657858
Wang SYang YYang JLiu J(2024)A Dynamic Collaborative Recommendation Method Based on Multimodal FusionAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5663-6_1(3-14)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5663-6_1
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Computer Science Review Volume 51, Issue C

Feb 2024

216 pages

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Zeng ZWang YZhao YShi W(2024)A Survey of Music Recommendation SystemsProceedings of the 5th International Conference on Computer Information and Big Data Applications10.1145/3671151.3671243(507-519)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3671151.3671243
Martinez ATufis MBoratto LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Unmasking Privacy: A Reproduction and Evaluation Study of Obfuscation-based Perturbation Techniques for Collaborative FilteringProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657858(1753-1762)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657858
Wang SYang YYang JLiu J(2024)A Dynamic Collaborative Recommendation Method Based on Multimodal FusionAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5663-6_1(3-14)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5663-6_1
Wang JMa H(2024)Users’ Preference-Aware Music Recommendation with Contrastive LearningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5615-5_25(309-320)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5615-5_25

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