论文标题
可解释的自动编码器，用于协作过滤建议
An Explainable Autoencoder For Collaborative Filtering Recommendation
论文作者
论文摘要
自动编码器是深度学习体系结构的常见构建基础，它们主要用于表示学习。它们也已成功用于协作过滤（CF）推荐系统，以预测丢失的评分。不幸的是，像所有黑匣子机器学习模型一样，他们无法解释其输出。因此，虽然基于自动编码器的建议系统的预测可能是准确的，但用户可能尚不清楚为什么会生成建议。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建议系统，可以使用基于邻里的解释样式来解释其预测。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体系结构的第一步。
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.
