  Autoencoders are a common building block of Deep Learning architectures,
where they are mainly used for representation learning. They have also been
successfully used in Collaborative Filtering (CF) recommender systems to
predict missing ratings. Unfortunately, like all black box machine learning
models, they are unable to explain their outputs. Hence, while predictions from
an Autoencoder-based recommender system might be accurate, it might not be
clear to the user why a recommendation was generated. In this work, we design
an explainable recommendation system using an Autoencoder model whose
predictions can be explained using the neighborhood based explanation style.
Our preliminary work can be considered to be the first step towards an
explainable deep learning architecture based on Autoencoders.
