Layer-wise Relevance Propagation for Explainable Recommendations

Understand

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.