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Explainability in Music Recommender Systems.

, , , , , и . AI Mag., 43 (2): 190-208 (2022)

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Alessandro Valeri

Fluorescence resonance energy transfer between multiple chromophores studied by single-molecule spectroscopy. Uni Düsseldorf, (2009)

Alessandro Zivelonghi

Alessandro Nova

Alessandro Topa

Alessandro Mirizzi

 

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Investigating gender fairness of recommendation algorithms in the music domain., , , , , и . Inf. Process. Manag., 58 (5): 102666 (2021)Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?, , , , , , и . RecSys, стр. 601-606. ACM, (2021)Explainability in Music Recommender Systems., , , , , и . CoRR, (2022)EmoMTB: Emotion-aware Music Tower Blocks., , , , , , , , и . ICMR, стр. 206-210. ACM, (2022)Personality Bias of Music Recommendation Algorithms., , и . RecSys, стр. 533-538. ACM, (2020)Pandemics, music, and collective sentiment: evidence from the outbreak of COVID-19., , , , и . ISMIR, стр. 157-165. (2020)Personality Correlates of Music Audio Preferences for Modelling Music Listeners., и . UMAP, стр. 313-317. ACM, (2020)Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation., , , , , , , , и . Int. J. Multim. Inf. Retr., 12 (1): 13 (июня 2023)LEMONS: Listenable Explanations for Music recOmmeNder Systems., , , и . ECIR (2), том 12657 из Lecture Notes in Computer Science, стр. 531-536. Springer, (2021)Exploring Cross-group Discrepancies in Calibrated Popularity for Accuracy/Fairness Trade-off Optimization., , , , , , и . MORS@RecSys, том 3268 из CEUR Workshop Proceedings, CEUR-WS.org, (2022)