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Song embeddings are a key component of most music recommendation engines.
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2017
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M. Schedl and B. Ferwerda, “Large-scale analysis of group-specific music genre taste from collaborative tags,” in 2017 IEEE International Symposium on Multimedia (ISM) , 2017, pp. 479–482
2017
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2018
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H. Caselles-Dupré, F. Lesaint, and J. Royo-Letelier, “Word2vec applied to recommendation: Hyperparameters matter,” in Proceedings of the 12th ACM Conference on Recommender Systems , ser. RecSys ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 352–356. [Online]. Available: https://doi.org/10.1145/3240323.3240377
2018
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2018
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2019
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2019
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2019
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2019
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2020
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P. Tovstogan, X. Serra, and D. Bogdanov, “Web interface for exploration of latent and tag spaces in music auto-tagging,” in Proceedings of the 37 th International Conference on Machine Learning; 2020 Jul 13-18; Vienna, Austria.[Vienna]: ICML; 2020.[3 p.] . ICML, 2020
2020
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2020
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2020
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2021
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M. Won, S. Oramas, O. Nieto, F. Gouyon, and X. Serra, “Multimodal metric learning for tag-based music retrieval,” in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021, Toronto, ON, Canada, June 6-11, 2021 . IEEE, 2021, pp. 591–595. [Online]. Available: https://doi.org/10.1109/ICASSP39728.2021.9413514
2021
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S. Doh, J. Lee, and J. Nam, “Million song search: Web interface for semantic music search using musical word embedding,” in International Society for Music Information Retrieval Conference, ISMIR 2021 . International Society for Music Information Retrieval, 2021
2021
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2021
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K. Chen, B. Liang, X. Ma, and M. Gu, “Learning audio embeddings with user listening data for content-based music recommendation,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 3015–3019
2021
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F. Korzeniowski, S. Oramas, and F. Gouyon, “Artist similarity using graph neural networks,” in Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR 2021, Online, November 7-12, 2021 , J. H. L. 0001, A. L. 0001, Z. Duan, J. Nam, P. Rao, P. van Kranenburg, and A. Srinivasamurthy, Eds., 2021, pp. 350–357. [Online]. Available: https://archives.ismir.net/ismir2021/paper/000043.pdf
2021
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2022
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