  We present our solution for the EvalRS DataChallenge. The EvalRS
DataChallenge aims to build a more realistic recommender system considering
accuracy, fairness, and diversity in evaluation. Our proposed system is based
on an ensemble between an item-based variational auto-encoder (VAE) and a
Bayesian personalized ranking matrix factorization (BPRMF). To mitigate the
bias in popularity, we use an item-based VAE for each popularity group with an
additional fairness regularization. To make a reasonable recommendation even
the predictions are inaccurate, we combine the recommended list of BPRMF and
that of item-based VAE. Through the experiments, we demonstrate that the
item-based VAE with fairness regularization significantly reduces popularity
bias compared to the user-based VAE. The ensemble between the item-based VAE
and BPRMF makes the top-1 item similar to the ground truth even the predictions
are inaccurate. Finally, we propose a `Coefficient Variance based Fairness' as
a novel evaluation metric based on our reflections from the extensive
experiments.
