Preference-based and local post-hoc explanations for recommender systems

Abstract

Post-hoc explanation aims at defining a simple local surrogate model to shed light on a prediction produced by a complex, generally black-box, model. In the general context of classification, it has been shown that local surrogates may not always be able to capture a local explanation, i.e. for a specific instance prediction, but rather depict more of a general behavior of the black-box. This problem is even more complex in a recommendation scenario where classes and decision boundaries are not explicitly defined and where data are very sparse by nature. We show in this paper that it is possible to tackle these problems with an efficient sampling around the recommendation instance to explain, to finally learn a proper local surrogate model. To this aim, this paper introduces several new approaches to capture efficiently local explanation models in the context of recommendation, all defined around a locality sample. Noticeably, and novel to this work, we show that it is possible to achieve a simple, yet better quality explanation model by not directly considering ratings, but rather implicit preferences as expressed by comparisons of pairs of ratings. We introduce to this extent a novel explainable model based on a pairwise loss RankNet architecture. Extensive experiments show that our methods can be better than state-of-the-art methods depending on the locality of the black-box model, and are much more efficient to retrieve meaningful explainable features locally.