Organization:ListenBrainz
ListenBrainz is a free and open source project that aims to crowdsource listening data from digital music and release it under an open license.[1] It is a MetaBrainz Foundation project tied to MusicBrainz. It aims to re-implement Last.fm features that were lost following that platform's acquisition by CBS.[2][3]
ListenBrainz takes submissions from media players and services such as Music Player Daemon, Spotify, and Rhythmbox in the form of listens. ListenBrainz can also import Last.fm and Libre.fm scrobbles in order to build listening history. As listens are released under an open license, ListenBrainz is useful for music research for industry and development purposes.[4][5][6][7][8]
ListenBrainz can also generate recommendations and playlists based on individual listening.[9]
In December 2021, the Year in Music reports feature was added, allowing users to find out and share their top tracks, albums, and artists for the year.[10]
References
- ↑ "ListenBrainz Goals". https://listenbrainz.org/goals.
- ↑ O'Brien, Danny (3 June 2021). "Organizing in the Public Interest: MusicBrainz". https://www.eff.org/deeplinks/2021/06/organizing-public-interest-musicbrainz.
- ↑ Vigliensoni, Gabriel; Fujinaga, Ichiro (23 October 2017). "The Music Listening Histories Dataset". Proceedings of the 18th International Society for Music Information Retrieval Conference (Suzhou, China: ISMIR): 96-102. doi:10.5281/zenodo.1417499. https://zenodo.org/records/1417499. Retrieved 17 February 2024.
- ↑ Singh, Param; Kamlesh, Dutta; Kaye, Robert; Garg, Suyash (2020). "Music Listening History Dataset Curation and Distributed Music Recommendation Engines Using Collaborative Filtering". Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering. 605. pp. 623–632. doi:10.1007/978-3-030-30577-2_55. ISBN 978-3-030-30576-5. https://link.springer.com/chapter/10.1007/978-3-030-30577-2_55. Retrieved 13 February 2021.
- ↑ Yadav, Naina; Singh, Anil (December 2020). "Bi-directional Encoder Representation of Transformer model for Sequential Music Recommender System". Forum for Information Retrieval Evaluation. pp. 49–53. doi:10.1145/3441501.3441503. ISBN 9781450389785. https://dl.acm.org/doi/abs/10.1145/3441501.3441503. Retrieved 13 February 2021.
- ↑ Schedl, Markus; Knees, Peter; McFee, Brian; Bogdanov, Dmitry (22 November 2021). "Music Recommendation Systems: Techniques, Use Cases, and Challenges". Recommender Systems Handbook: 927–971. doi:10.1007/978-1-0716-2197-4_24. ISBN 978-1-0716-2196-7. https://link.springer.com/chapter/10.1007/978-1-0716-2197-4_24. Retrieved 9 December 2023.
- ↑ Pocaro, Lorenzo; Gómez, Emilia; Castillo, Carlos (12 July 2023). "Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study". ACM Transactions on Recommender Systems. doi:10.1145/3608487. https://dl.acm.org/doi/10.1145/3608487. Retrieved 17 February 2024.
- ↑ Ray, Brian (6 December 2019). "Build a useful ML Model in hours on GCP to Predict The Beatles’ listeners". Towards Data Science Inc.. https://towardsdatascience.com/build-a-useful-ml-model-in-hours-on-gcp-to-predict-the-beatles-listeners-1b2322486bdf.
- ↑ Porter, Alastair (24 December 2020). "Playlists and personalised recommendations in ListenBrainz". MetaBrainz Foundation. https://blog.metabrainz.org/2020/12/24/playlists-and-personalised-recommendations-in-listenbrainz/.
- ↑ "ListenBrainz presents: Your Year in Music" (in en). 2021-12-16. https://blog.metabrainz.org/2021/12/16/listenbrainz-presents-your-year-in-music/.
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