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Overview of the third international workshop on search and mining user-generated contents.

, , , , , and . CIKM, page 2625-2626. ACM, (2011)

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Modeling Popularity and Temporal Drift of Music Genre Preferences., , and . Trans. Int. Soc. Music. Inf. Retr., 3 (1): 17-30 (2020)Investigating gender fairness of recommendation algorithms in the music domain., , , , , and . Inf. Process. Manag., 58 (5): 102666 (2021)ReuseKNN: Neighborhood Reuse for Differentially Private KNN-Based Recommendations., , , and . ACM Trans. Intell. Syst. Technol., 14 (5): 80:1-80:29 (October 2023)An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation., , , and . ACM Trans. Intell. Syst. Technol., 10 (5): 57:1-57:21 (2019)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)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)Personality & Emotional States: Understanding Users' Music Listening Needs., , and . UMAP Workshops, volume 1388 of CEUR Workshop Proceedings, CEUR-WS.org, (2015)