Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders
This addresses fairness in personalized recommendations for unseen users, but it is incremental as it builds on existing VAE methods.
The paper tackled the problem of providing fair recommendations to new users not in the training data by limiting demographic information encoding in Variational Autoencoder-based recommender systems, achieving this capability as evaluated.
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.