A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering

  title={A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering},
  author={Tommaso Carraro and Mirko Polato and Fabio Aiolli},
  journal={Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization},
Deep learning-based recommender systems are nowadays defining the state-of-the-art. Unfortunately, their hard interpretability restrains their application in scenarios in which explainability is required/desirable. Many efforts have been devoted to injecting explainable information inside deep models. However, there is still a lot of work that needs to be done to fill this gap. In this paper, we take a step in this direction by providing an intuitive interpretation of the inner representation… 
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