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Unlearning Protected User Attributes in Recommendations with Adversarial Training.

, , , , and . SIGIR, page 2142-2147. ACM, (2022)

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Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity., , and . CMMR, volume 3902 of Lecture Notes in Computer Science, page 196-200. Springer, (2005)Investigating Web-Based Approaches to Revealing Prototypical Music Artists in Genre Taxonomies., , and . ICDIM, page 519-524. IEEE, (2006)The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study., , and . CoRR, (2019)Modeling Popularity and Temporal Drift of Music Genre Preferences., , and . Trans. Int. Soc. Music. Inf. Retr., 3 (1): 17-30 (2020)Affect in Multimedia: Benchmarking Violent Scenes Detection., , , , , , and . IEEE Trans. Affect. Comput., 13 (1): 347-366 (2022)Support the underground: characteristics of beyond-mainstream music listeners., , , , , and . EPJ Data Sci., 10 (1): 14 (2021)Movie genome: alleviating new item cold start in movie recommendation., , , , , , , and . User Model. User Adapt. Interact., 29 (2): 291-343 (2019)On the Interrelation Between Listener Characteristics and the Perception of Emotions in Classical Orchestra Music., , , , , and . IEEE Trans. Affect. Comput., 9 (4): 507-525 (2018)Music Information Retrieval: Recent Developments and Applications., , and . Found. Trends Inf. Retr., 8 (2-3): 127-261 (2014)Supervised and Unsupervised Web Document Filtering Techniques to Improve Text-Based Music Retrieval., , , , and . ISMIR, page 543-548. International Society for Music Information Retrieval, (2010)