Associated with the Linz Institute of Technology and the Institute of Computational Perception @ JKU Linz, Austria

Music4All-Onion -- A Large-Scale Multi-Faceted Content-Centric Music Recommendation

Music4AllOnion Teaser

Abstract

When 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 kind of information is, however, still frequently neglected by mainstream recommendation models based on collaborative filtering that rely solely on user-item interactions to recommend items to users. A major reason for this neglect is the lack of standardized datasets that provide both collaborative and content information. The work at hand addresses this shortcoming by introducing Music4All-Onion, a large-scale, multi-modal music dataset. The dataset expands the Music4All dataset by including 26 additional audio, video, and metadata characteristics for 109,269 music pieces. In addition, it provides a set of 252,984,396 listening records of 119,140 users, extracted from the online music platform Last.fm, which allows leveraging user-item interactions as well. We organize distinct item content features in an onion model according to their semantics, and perform a comprehensive examination of the impact of different layers of this model (e.g., audio features, user-generated content, and derivative content) on content-driven music recommendation, demonstrating how various content features influence accuracy, novelty, and fairness of music recommendation systems. In summary, with Music4All-Onion, we seek to bridge the gap between collaborative filtering music recommender systems and content-centric music recommendation requirements.


Citation

Marta Moscati, Emilia Parada-Cabaleiro, Yashar Deldjoo, Eva Zangerle, Markus Schedl
Music4All-Onion -- A Large-Scale Multi-Faceted Content-Centric Music Recommendation
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM), 4339–4343, doi:10.1145/3511808., 2022.

BibTeX

@article{Moscati2022Music4AllOnion,
    title = {Music4All-Onion -- A Large-Scale Multi-Faceted Content-Centric Music Recommendation},
    author = {Moscati, Marta and Parada-Cabaleiro, Emilia and Yashar Deldjoo and Eva Zangerle and Schedl, Markus},
    booktitle = {Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM)},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    doi = {10.1145/3511808.},
    url = {https://doi.org/10.1145/3511808.3557656},
    pages = {4339–4343},
    location = {Atlanta, GA, USA},
    year = {2022}
}