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The rapid adoption of large language models (LLMs) in recommender systems (RS) presents new challenges in understanding and evaluating their biases, which can result in unfairness or the amplification of stereotypes.
Fairness in recommender systems,
M. D. Ekstrand, A. Das, R. Burke, F. Diaz, · 2012
Earlier work this paper cites.
User-oriented fairness in recommendation,
Y. Li, H. Chen, Z. Fu, Y. Ge, Y. Zhang, · 2021
Earlier work this paper cites.
Fairness in information access systems,
M. D. Ekstrand, A. Das, R. Burke, F. Diaz, · 2022
Earlier work this paper cites.
Consumer fairness in recommender systems: Contextualizing definitions and mitigations,
L. Boratto, G. Fenu, M. Marras, G. Medda, · 2022
Earlier work this paper cites.
Large language models as zero-shot conversational recommenders,
Z. He, Z. Xie, R. Jha, H. Steck, D. Liang, Y. Feng, B. P. Majumder, N. Kallus, J. McAuley, · 2023
Cited alongside, same era.
Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation,
J. Zhang, K. Bao, Y. Zhang, W. Wang, F. Feng, X. He, · 2023
Cited alongside, same era.
Fairness in recommender systems: research landscape and future directions,
Y. Deldjoo, D. Jannach, A. Bellogin, A. Difonzo, D. Zanzonelli, · 2024
Cited alongside, same era.
A review of modern recommender systems using generative models (gen-recsys),
Y. Deldjoo, Z. He, J. McAuley, A. Korikov, S. Sanner, A. Ramisa, R. Vidal, M. Sathiamoorthy, A. Kasirzadeh, S. Milano, · 2024
Closest in time.
Cfairllm: Consumer fairness evaluation in large-language model recommender system,
Y. Deldjoo, T. Di Noia, · 2024
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Y. Deldjoo, · 2024
Closest in time.
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