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 Autoencoder-based 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.

中文翻译：

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用于协同过滤推荐的可解释自动编码器

自编码器是深度学习架构的常见构建块，主要用于表示学习。它们还成功地用于协作过滤 (CF) 推荐系统以预测缺失的评分。不幸的是，与所有黑盒机器学习模型一样，它们无法解释其输出。因此，虽然来自基于自动编码器的推荐系统的预测可能是准确的，但用户可能不清楚为什么生成推荐。在这项工作中，我们使用自动编码器模型设计了一个可解释的推荐系统，其预测可以使用基于邻域的解释风格来解释。我们的初步工作可以被认为是迈向基于自动编码器的可解释深度学习架构的第一步。