Providing Previously Unseen Users Fair Recommendations Using Variational
  Autoencoders
Bjørnar Vassøy Helge Langseth Benjamin Kille
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.