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Counterfactual Music Recommendation for Mitigating Popularity Bias

Bibliographic Data

ID22108494
AuthorsJidong Yuan (0000-0003-2654-3372, Beijing Jiaotong University), Bingyu Gao (0009-0008-3586-4956, Beijing Jiaotong University), Xiaokang Wang (0009-0004-8691-5865, Beijing University of Posts and Telecommunications), Haiyang Liu (0000-0002-6900-3491, Beijing Jiaotong University), Lingyin Zhang (0009-0007-2780-8586, Beijing Jiaotong University)
Year2025
Volume12
Issue2
Pages851-861
Publication date2025-04-01
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2024.3491800
OpenAlexW4404914693
LanguageEN
Citations received2
References cited23

Music recommendation systems aim to suggest tracks that users may enjoy. However, the accuracy of recommendation results is affected by popularity bias. Previous studies have focused on mitigating the direct effect of single-item popularity in video, news, or e-commerce recommendations, but have overlooked the multisource popularity biases in music recommendations. This article proposes a causal inference-based method to reduce the influence of both track and artist popularity. First, we construct a causal graph that encompasses users, tracks, and artists within the context of music recommendations. Next, we employ matrix factorization in conjunction with counterfactual inference theory to mitigate the popularity effects of artists and tracks, taking into account both the natural direct and indirect effects of these entities on music recommendations. Experimental results evaluated on four music recommendation datasets indicate that our method outperforms other baselines and effectively alleviates the popularity bias of both tracks and artists

Computer security · Counterfactual thinking · Exploit · Popularity · Computer Science · Music and Audio Processing · Music History and Culture · Music Technology and Sound Studies · Psychology · Social Psychology

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Citations per year2
Citation span2025 - 2025 (1)
Highly citedNo

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