复现推荐系统中属性级去标识方法，保障结果可复现性
Reproducibility Companion Paper: Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
- 复现先前提出的用户属性级去标识方法
- 提供完整数据预处理与实验配置细节
- 适合关注可复现性与隐私保护的研究者
本文复现了此前发表于ACM多媒体会议第31届会议上的论文《使用户不可区分：推荐系统中的属性级去标识》所展示的实验结果。研究旨在验证所提方法的有效性，并帮助其他研究者复现相关实验。本文详细描述了数据集预处理过程、源代码结构、配置文件设置、实验环境及复现后的实验结果，确保方法的可重复性与透明性。
原文摘要 · Abstract (English)
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.
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