A Deep Generative Recommendation Method for Unbiased Learning From Implicit Feedback

Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke

Abstract:

Variational autoencoders ( VAE s) are the state-of-the-art model for
recommendation with implicit feedback signals. Unfortunately, implicit feedback suffers from selection bias, e.g., popularity bias, position bias, etc., and as a result, training from such signals produces
biased recommendation models. Existing methods for debiasing
the learning process have not been applied in a generative setting.
We address this gap by introducing an inverse propensity scoring (IPS ) based method for training VAE s from implicit feedback
data in an unbiased way. Our IPS -based estimator for the VAE training objective, VAE-IPS, is provably unbiased w.r.t. selection bias.
Our experimental results show that the proposed VAE-IPS model
reaches significantly higher performance than existing baselines.
Our contributions enable practitioners to combine state-of-the-art
VAE recommendation techniques with the advantages of bias mitigation for implicit feedback.