The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study

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Luo, X., Zhou, M., Xia, Y., Zhu, Q.: An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics 10

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Jannach, D., Lerche, L., Kamehkhosh, I., Jugovac, M.: What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Modeling and User-Adapted Interaction 25

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Schedl, M.: The LFM-1B Dataset for Music Retrieval and Recommendation. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. pp. 103–110. ICMR ’16, ACM, New York, NY, USA (2016)

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Schedl, M., Zamani, H., Chen, C.W., Deldjoo, Y., Elahi, M.: Current challenges and visions in music recommender systems research. International Journal of Multimedia Information Retrieval 7

2018

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Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The unfairness of popularity bias in recommendation. In: Workshop on Recommendation in Multi-stakeholder Environments (RMSE’19), in conjunction with the 13th ACM Conference on Recommender Systems, RecSys’19 (2019)

2019

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Bauer, C., Schedl, M.: Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems. PloS one 14

2019

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Kowald, D., Lex, E., Schedl, M.: Modeling artist preferences for personalized music recommendations. In: Proceedings of the Late-Breaking-Results Track of the 20th Annual Conference of the International Society for Music Information Retrieval. ISMIR ’19 (2019)

2019