Computer Engineering and Computer Science
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 Autoencoderbased 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.
Haghighi, Pegah Sagheb; Seton, Olurotimi; and Nasraoui, Olfa, "An Explainable Autoencoder For Collaborative Filtering Recommendation" (2019). Faculty Scholarship. 429.