2024

Machine Unlearning for Recommendation Systems: An Insight

Sachdeva, Bhavika, Rathee, Harshita, Sristi et al.

Understand

This review explores machine unlearning (MUL) in recommendation systems, addressing adaptability, personalization, privacy, and bias challenges.

Unlike traditional models, MUL dynamically adjusts system knowledge based on shifts in user preferences and ethical considerations.
The paper critically examines MUL's basics, real-world applications, and challenges like algorithmic transparency.
It sifts through literature, offering insights into how MUL could transform recommendations, discussing user trust, and suggesting paths for future research in responsible and user-focused artificial intelligence (AI).

Built on

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Similar

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Then

Y. Li, C. Chen, X. Zheng, Y. Zhang, Z. Han, D. Meng, and J. Wang, “Making users indistinguishable: Attribute-wise unlearning in recommender systems,” in Proceedings of the 31st ACM International Conference on Multimedia , ser. MM ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 984–994. [Online]. Available: https://doi.org/10.1145/3581783.3612418
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J. Liu, J. Lou, Z. Qin, and K. Ren, “Certified minimax unlearning with generalization rates and deletion capacity,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023. [Online]. Available: https://openreview.net/forum?id=6H8Md75kAw
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Later among the works it cites.
J. Leysen, “Exploring unlearning methods to ensure the privacy, security, and usability of recommender systems,” in Proceedings of the 17th ACM Conference on Recommender Systems , ser. RecSys ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 1300–1304. [Online]. Available: https://doi.org/10.1145/3604915.3608862
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Later among the works it cites.
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