  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.
