Reproducibility Companion Paper: Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
On the map: LLM safety and security → Membership inference attacks
Abstract (arXiv)
In this paper, we reproduce the experimental results presented in our previous work titled "Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems," which was published in the proceedings of the 31st ACM International Conference on Multimedia. This paper aims to validate the effectiveness of our proposed method and help others reproduce our experimental results. We provide detailed descriptions of our preprocessed datasets, source code structure, configuration file settings, experimental environment, and reproduced experimental results.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Membership inference attacks, one of 12 directions in LLM safety and security. Builds on 1 of its 7 references that are in the map. First posted on arXiv 29 Mar 2025.
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1 of 7 references.
References · 1
- 2024Post-Training Attribute Unlearning in Recommender Systemscited by 24
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- 2023Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
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- 2022Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
- 2024CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
- 2024Machine Unlearning for Recommendation Systems: An Insight
- 2022Recommendation Unlearning
- 2024A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions
- 2025Customized Retrieval-Augmented Generation with LLM for Debiasing Recommendation Unlearning