Can I read Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders on EtoBox?

Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders by Vassøy, Bjørnar; Langseth, Helge; Kille, Benjamin is a scholarly article available to read on EtoBox.

What is Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders about?

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.

Author

Vassøy, Bjørnar; Langseth, Helge; Kille, Benjamin

Published

2023

Language

EN