论文标题
特征交互解释性：一种通过神经互动检测来解释AD-启用系统的案例
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
论文作者
论文摘要
建议是对许多用户影响的机器学习的普遍应用。因此，推荐模型必须准确且可解释很重要。在这项工作中，我们提出了一种解释和增强黑盒推荐系统预测的方法。特别是，我们建议从源建议模型中解释特征交互，并在目标推荐模型中明确编码这些交互，其中源和目标模型都是黑框。通过不假设推荐系统的结构，我们的方法可以在一般设置中使用。在我们的实验中，我们专注于机器学习推荐的明显使用：广告单击预测。我们发现，我们的交互解释既有信息又具有预测性，例如，表现明显优于现有的推荐模型。更重要的是，解释互动的相同方法甚至可以为领域提供新的见解，甚至超出建议，例如文本和图像分类。
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box recommender systems. In particular, we propose to interpret feature interactions from a source recommender model and explicitly encode these interactions in a target recommender model, where both source and target models are black-boxes. By not assuming the structure of the recommender system, our approach can be used in general settings. In our experiments, we focus on a prominent use of machine learning recommendation: ad-click prediction. We found that our interaction interpretations are both informative and predictive, e.g., significantly outperforming existing recommender models. What's more, the same approach to interpret interactions can provide new insights into domains even beyond recommendation, such as text and image classification.
