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musicology.bib
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musicology.bib
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@misc{lasar_million-song_2011,
title = {Million-song dataset: take it, it’s free},
url = {https://arstechnica.com/information-technology/2011/03/million-song-dataset-take-it-its-free/},
titleaddon = {Ars Technica},
author = {Lasar, Matthew},
date = {2011-03-08},
year = {2013}
}
@incollection{bertin-mahieux_million_2011,
title = {The Million Song Dataset},
url = {http://millionsongdataset.com/},
booktitle = {{Proceedings of the 12th International Conference on Music Information}},
author = {Bertin-Mahieux, Thierry and Ellis, Daniel P. W. and Whitman, Brian and Lamere, Paul},
year = {2011}
}
@inproceedings{spotify-collaborative-filtering_2020,
author = {Pérez-Marcos, Javier and Batista, Vivian},
year = {2018},
month = {06},
pages = {214-220},
title = {Recommender System Based on Collaborative Filtering for Spotify’s Users},
isbn = {978-3-319-61577-6},
doi = {10.1007/978-3-319-61578-3_22}
}
@article{werner_gender_spotify_2020,
author = {Ann Werner},
title = {Organizing music, organizing gender: algorithmic culture and Spotify recommendations},
journal = {Popular Communication},
volume = {18},
number = {1},
pages = {78-90},
year = {2020},
publisher = {Routledge},
doi = {10.1080/15405702.2020.1715980},
URL = {https://doi.org/10.1080/15405702.2020.1715980},
eprint = {https://doi.org/10.1080/15405702.2020.1715980}
}
@misc{kraemer_spotify_2020,
title = {Spotify, Apple Music, Deezer and {YouTube} found recently hosting racist music},
url = {https://www.bbc.com/news/newsbeat-54613907},
publisher = {{BBC} News},
author = {Kraemer, David and Holden, Steve},
date = {2020-10-22},
year = {2020},
keywords = {algorithm, racism, Spotify}
}
@inproceedings{music_personalization_spotify_2016,
author = {Jacobson, Kurt and Murali, Vidhya and Newett, Edward and Whitman, Brian and Yon, Romain},
title = {Music Personalization at Spotify},
year = {2016},
isbn = {9781450340359},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2959100.2959120},
doi = {10.1145/2959100.2959120},
booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems},
pages = {373},
numpages = {1},
keywords = {data pipelines, discover weekly, machine learning, music personalization, recommender system, spotify, collaborative filtering},
location = {Boston, Massachusetts, USA},
series = {RecSys '16}
}
@inproceedings{deep_youtube_recommendations_2016,
author = {Covington, Paul and Adams, Jay and Sargin, Emre},
title = {Deep Neural Networks for YouTube Recommendations},
year = {2016},
isbn = {9781450340359},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2959100.2959190},
doi = {10.1145/2959100.2959190},
abstract = {YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.},
booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems},
pages = {191–198},
numpages = {8},
keywords = {deep learning, scalability, recommender system},
location = {Boston, Massachusetts, USA},
series = {RecSys '16}
}
@inproceedings{cold_start_music_recommendation_2017,
author = {Oramas, Sergio and Nieto, Oriol and Sordo, Mohamed and Serra, Xavier},
title = {A Deep Multimodal Approach for Cold-Start Music Recommendation},
year = {2017},
isbn = {9781450353533},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3125486.3125492},
doi = {10.1145/3125486.3125492},
abstract = {An increasing amount of digital music is being published daily. Music streaming services often ingest all available music, but this poses a challenge: how to recommend new artists for which prior knowledge is scarce? In this work we aim to address this so-called cold-start problem by combining text and audio information with user feedback data using deep network architectures. Our method is divided into three steps. First, artist embeddings are learned from biographies by combining semantics, text features, and aggregated usage data. Second, track embeddings are learned from the audio signal and available feedback data. Finally, artist and track embeddings are combined in a multimodal network. Results suggest that both splitting the recommendation problem between feature levels (i.e., artist metadata and audio track), and merging feature embeddings in a multimodal approach improve the accuracy of the recommendations.},
booktitle = {Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems},
pages = {32–37},
numpages = {6},
keywords = {music, deep learning, multimodal, recommender systems, semantics},
location = {Como, Italy},
series = {DLRS 2017}
}
@misc{spotify_amplifying_input_2020,
title = {Amplifying Artist Input in Your Personalized Recommendations},
url = {https://newsroom.spotify.com/2020-11-02/amplifying-artist-input-in-your-personalized-recommendations/},
publisher = {{Newsroom} on {Spotify.com}},
author = {{Spotify}},
date = {2020-11-02},
year = {2020},
keywords = {algorithm, Spotify}
}
@online{johnston_how_2018,
title = {How Spotify Discovers the Genres of Tomorrow},
url = {https://artists.spotify.com/blog/how-spotify-discovers-the-genres-of-tomorrow},
titleaddon = {Spotify for Artists Blog},
author = {Johnston, Maura},
urldate = {2020-12-05},
date = {2018-06-07},
year = {2018}
}
@article{Vagnerova_Garcia_Molina_2018,
title={Academic Labor and Music Curricula},
url={https://journals.library.columbia.edu/index.php/currentmusicology/article/view/5366},
DOI={10.7916/cm.v0i102.5366},
number={102},
journal={Current Musicology},
author={Vágnerová, Lucie AND García Molina, Andrés},
year={2018},
month={Apr}
}
@article{garcia_molina_2016,
title = {Labor and the Performance of Place in the Upper Putumayo},
url = {https://www.sibetrans.com/trans/articulo/522/labor-and-the-performance-of-place-in-the-upper-putumayo},
journal = {TRANS Revista Transcultural de Música - Transcultural Music Review},
year = {2016},
number = {20},
month = {12}
}
@article{garcia_molina_2020,
author = {Andrés García Molina},
title = {Nostalgia, internal migration and the return of Cuban street-vendor songs},
journal = {Culture, Theory and Critique},
volume = {61},
number = {2-3},
pages = {229-245},
year = {2020},
publisher = {Routledge},
doi = {10.1080/14735784.2020.1828119},
URL = {
https://doi.org/10.1080/14735784.2020.1828119
},
eprint = {
https://doi.org/10.1080/14735784.2020.1828119
}
}
@online{pelly_streambait_2018,
title = {Streambait Pop},
url = {https://thebaffler.com/latest/streambait-pop-pelly},
abstract = {The Spotify economy of clicks and completions, where the most precious commodity is polarized human attention, has given rise to a new streambait pop.},
titleaddon = {The Baffler},
author = {Pelly, Liz},
urldate = {2022-04-11},
date = {2018-12-11},
year = {2018},
month = {12},
day = {11},
langid = {american}
}