同时消除用户性别与年龄等多类隐私属性，提升推荐模型公平性与隐私保护。
Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training
- 用变分自编码器结合对抗训练，同步移除用户嵌入中的多类敏感属性。
- 在两个数据集上验证，比单属性移除方法更有效降低偏见并增强嵌入匿名性。
- 适合关注推荐系统公平性与用户隐私保护的研究者与工程师。
在广泛使用的基于神经网络的协同过滤模型中，用户历史行为被编码为表征其偏好的潜在嵌入。在此设置下，模型即使未显式访问用户敏感属性（如性别或种族），也能从用户嵌入中推断出这些属性，导致对特定群体的不公平对待并引发隐私问题。以往工作仅能逐个移除单一敏感属性，而现实场景中多个属性可能同时存在。本文提出 AdvXMultVAE，旨在通过对抗训练（AdvMultVAE）框架，同时移除用户嵌入中的性别与年龄等多类敏感属性（支持连续与类别值）。实验在音乐领域 LFM-2b-100k 和电影领域 Ml-1m 两个数据集上进行，结果表明，该方法在缓解群体偏见、提升嵌入匿名性方面均优于单属性移除基线。
原文摘要 · Abstract (English)
In widely used neural network-based collaborative filtering models, users' history logs are encoded into latent embeddings that represent the users' preferences. In this setting, the models are capable of mapping users' protected attributes (e.g., gender or ethnicity) from these user embeddings even without explicit access to them, resulting in models that may treat specific demographic user groups unfairly and raise privacy issues. While prior work has approached the removal of a single protected attribute of a user at a time, multiple attributes might come into play in real-world scenarios. In the work at hand, we present AdvXMultVAE which aims to unlearn multiple protected attributes (exemplified by gender and age) simultaneously to improve fairness across demographic user groups. For this purpose, we couple a variational autoencoder (VAE) architecture with adversarial training (AdvMultVAE) to support simultaneous removal of the users' protected attributes with continuous and/or categorical values. Our experiments on two datasets, LFM-2b-100k and Ml-1m, from the music and movie domains, respectively, show that our approach can yield better results than its singular removal counterparts (based on AdvMultVAE) in effectively mitigating demographic biases whilst improving the anonymity of latent embeddings.
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