  In this paper, we tackle the problem of explanations in a deep-learning based
model for recommendations by leveraging the technique of layer-wise relevance
propagation. We use a Deep Convolutional Neural Network to extract relevant
features from the input images before identifying similarity between the images
in feature space. Relationships between the images are identified by the model
and layer-wise relevance propagation is used to infer pixel-level details of
the images that may have significantly informed the model's choice. We evaluate
our method on an Amazon products dataset and demonstrate the efficacy of our
approach.
