An emerging definition of fairness in machine learning requires that models are oblivious to demographic user information, e.g., a user's gender or age should not influence the model. Personalized recommender systems are particularly prone to violating this definition through their explicit user focus and user modelling. Explicit user modelling is also an aspect that makes many recommender systems incapable of providing hitherto unseen users with recommendations. We propose novel approaches for mitigating discrimination in Variational Autoencoder-based recommender systems by limiting the encoding of demographic information. The approaches are capable of, and evaluated on, providing users that are not represented in the training data with fair recommendations.

翻译：机器学习中新兴的公平性定义要求模型忽略用户的人口统计学信息，例如用户的性别或年龄不应影响模型。个性化推荐系统因其明确的用户导向和用户建模，尤其容易违反这一定义。明确的用户建模也是许多推荐系统无法向未见过用户提供推荐的原因之一。我们提出了一种新颖方法，通过限制人口统计学信息的编码来减轻基于变分自编码器的推荐系统中的歧视问题。该方法能够—且已在实验中得到验证—为训练数据中未出现的用户提供公平推荐。