Recommendation is a prevalent application of machine learning that affects many users; therefore, it is crucial 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 extract feature interaction interpretations 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, i.e., significantly outperforming existing recommender models. What's more, the same approach to interpreting interactions can  provide new insights into domains even beyond recommendation. Despite their impact on users, state-of-the-art recommender systems are becoming increasingly inscrutable. For example, the models that predict if a user will click on an online advertisement are often based on function approximators that contain complex components in order to achieve optimal recommendation accuracy. The complex components come in the form of modules for better learning relationships among features, such as interactions between user and ad features (Cheng et al., 2016; Guo et al., 2017; Wang et al., 2017; Lian et al., 2018; Song et al., 2018) . Although efforts have been made to understand the feature relationships, there is still no method that can interpret the feature interactions learned by a generic recommender system, nor is there a strong commercial incentive to do so. In this work, we identify and leverage feature interactions that represent how a recommender system generally behaves. We propose a novel approach, Global Interaction Detection and Encoding for Recommendation (GLIDER), which detects feature interactions that span globally across multiple data-instances from a source recommender model, then explicitly encodes the interactions in a target recommender model, both of which can be black-boxes. GLIDER achieves this by first utilizing feature interaction detection with a data-instance level interpretation method called LIME (Ribeiro et al., 2016 ) over a batch of data samples. GLIDER then explicitly encodes the collected global interactions into a target model via sparse feature crossing. In our experiments on ad-click recommendation, we found that the interpretations generated by GLIDER are informative, and the detected global interactions can significantly improve the target model's prediction performance, even in a setting where the source and target models are the same. Because our interaction interpretation method is very general, we also show that the interpretations are informative in domains outside of recommendation, such as image and text classification. Our contributions are as follows: 1. We propose GLIDER to detect and explicitly encode global feature interactions in blackbox recommender systems. We proposed GLIDER that detects and explicitly encodes global feature interactions in black-box recommender systems. In our experiments, we found that the detected global interactions are informative and that explicitly encoding interactions can improve the accuracy of CTR predictions. We further validated interaction interpretations on image, text, and graph classifiers. We hope GLIDER encourages investigation into the complex interaction behaviors of recommender models to understand why certain feature interactions are very predictive. For future research, we wish to understand how feature interactions play a role in the integrity of automatic recommendations.
Proposed a method to extract and leverage interpretations of feature interactions
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