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Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e.
The unfairness of popularity bias in recommendation
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Being Accurate is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems. In CHI ’06 Extended Abstracts on Human Factors in Computing Systems (CHI EA ’06) . Association for Computing Machinery, New York, NY, USA, 1097–1101
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The Long Tail of Recommender Systems and How to Leverage It
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Comparison of implicit and explicit feedback from an online music recommendation service. In Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2010, Held at the 4th ACM Conference on Recommender Systems (RecSys 2010) . 47–51
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A Top-N Recommender System Evaluation Protocol Inspired by Deployed Systems
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Roadmap for Music Information ReSearch
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Big Data’s Disparate Impact
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An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems
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What’s Broken in Music Informatics Research? Three Uncomfortable Statements. In Proceedings of the 36th International Conference on Machine Learning . 2012–2014
Justin Salamon. 2019 · 2014
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All The Cool Kids, How Do They Fit In? Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. In Proceedings of the 1st ACM Conference on Fairness, Accountability and Transparency (ACM FAccT 2018) , Vol. 81. 172–186
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Bias Disparity in Recommendation Systems
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Fairness-Aware Tensor-Based Recommendation. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM ’18) . Association for Computing Machinery, New York, NY, USA, 1153–1162
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F. Maxwell Harper and Joseph A. Konstan. 2015 · 2015
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What recommenders recommend: an analysis of recommendation biases and possible countermeasures
Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, and Michael Jugovac. 2015 · 2015
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Adaptive multi-attribute diversity for recommender systems
Tommaso Di Noia, Jessica Rosati, Paolo Tomeo, and Eugenio Di Sciascio. 2017 · 2016
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The LFM-1b Dataset for Music Retrieval and Recommendation. In Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval (New York, New York, USA) (ICMR ’16) . Association for Computing Machinery, New York, NY, USA, 103–110
Markus Schedl. 2016 · 2016
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Surprise, a Python library for recommender systems
Nicolas Hug. 2017 · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai Wei Chang. 2017 · 2017
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Playlisting Favorites: Is Spotify Gender-Biased?
Luis Aguiar, Joel Waldfogel, and Sarah Waldfogel. 2018 · 2018
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Popular music lyrics and musicians’ gender over time: A computational approach
Manuel Anglada-Tort, Amanda E Krause, and Adrian C North. 2019 · 2019
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Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems
Christine Bauer and Markus Schedl. 2019 · 2019
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FaiRecSys: mitigating algorithmic bias in recommender systems
Bora Edizel, Francesco Bonchi, Sara Hajian, André Panisson, and Tamir Tassa. 2019 · 2019
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Fairness, Accountability and Transparency in Music Information Research (FAT-MIR)
Emilia Gomez, Andre Holzapfel, Marius Miron, and Bob L. Sturm. 2019 · 2019
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Towards More Impactful Recommender Systems Research. In Proceedings of the ImpactRS Workshop, 13th ACM Conference on Recommender Systems (RecSys 2019) . 15–17
Dietmar Jannach, Oren Sar Shalom, and Joseph A Konstan. 2019 · 2019
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Invisible Women: Exposing data bias in a world designed for men
Caroline Criado Perez. 2019 · 2019
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Discriminating Systems: Gender, Race and Power in AI
Sarah Myers West, Meredith Whittaker, and Kate Crawford. 2019 · 2019
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Offline evaluation options for recommender systems
Rocío Cañamares, Pablo Castells, and Alistair Moffat. 2020 · 2020
Closest in time.
Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information. In Proceedings of the 3rd ACM Conference on Fairness, Accountability and Transparency (ACM FAccT 2020) . Barcelona, Spain, 436–445
Sarah Dean, Sarah Rich, and Benjamin Recht. 2020 · 2020
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The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study. In Advances in Information Retrieval , Joemon M Jose, Emine Yilmaz, João Magalhães, Pablo Castells, Nicola Ferro, Mário J Silva, and Flávio Martins (Eds.). Springer International Publishing, Cham, 35–42
Dominik Kowald, Markus Schedl, and Elisabeth Lex. 2020 · 2020
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