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LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis

, , , , , and . ACM SIGIR Conference on Human Information Interaction and Retrieval, ACM, (March 2022)
DOI: 10.1145/3498366.3505791

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Mining User Generated Data for Music Information Retrieval., , , and . Mining User Generated Content, Chapman and Hall/CRC, (2014)Introduction to Intelligent Music Systems and Applications., , and . ACM TIST, 8 (2): 17:1-17:8 (2017)Personality Traits and Music Genres: What Do People Prefer to Listen To?, , and . UMAP, page 285-288. ACM, (2017)Genre-based Analysis of Social Media Data on Music Listening Behavior: Are Fans of Classical Music Really Averse to Social Media?, and . WISMM, page 9-13. ACM, (2014)A personality-based adaptive system for visualizing classical music performances., , , , , and . MMSys, page 15:1-15:7. ACM, (2016)Three web-based heuristics to determine a person's or institution's country of origin., , , , and . SIGIR, page 801-802. ACM, (2010)Automatically Adapting the Structure of Audio Similarity Spaces, , , and . Proceedings of the 1st Workshop on Learning the Semantics of Audio Signals, Athens, Greece, (2006)The Million Musical Tweet Dataset - What We Can Learn From Microblogs., , , and . ISMIR, page 189-194. (2013)Retrieving Relevant and Diverse Movie Clips Using the MFVCD-7K Multifaceted Video Clip Dataset., and . CBMI, page 1-4. IEEE, (2019)Movie Genome Recommender: A Novel Recommender System Based on Multimedia Content., , and . CBMI, page 1-4. IEEE, (2019)