  Recommendation systems are an integral part of Artificial Intelligence (AI)
and have become increasingly important in the growing age of commercialization
in AI. Deep learning (DL) techniques for recommendation systems (RS) provide
powerful latent-feature models for effective recommendation but suffer from the
major drawback of being non-interpretable. In this paper we describe a
framework for explainable temporal recommendations in a DL model. We consider
an LSTM based Recurrent Neural Network (RNN) architecture for recommendation
and a neighbourhood-based scheme for generating explanations in the model. We
demonstrate the effectiveness of our approach through experiments on the
Netflix dataset by jointly optimizing for both prediction accuracy and
explainability.
