Model-Agnostic Counterfactual Explanations of Recommendations

Explanations for algorithmically generated recommendations is an important requirement for transparent and trustworthy recommender systems. When the internal recommendation model is not inherently interpretable (e.g., most contemporary systems are complex and opaque), or when access to the system is not available (e.g., recommendation as a service), explanations have to be generated post-hoc, i.e., after the system is trained. In this common setting, the standard approach is to provide plausible interpretations of the observed outputs of the system, e.g., by building a simple surrogate model that is inherently interpretable, and explaining that model. This however has several drawbacks. First, such explanations are not truthful, as they are rationalizations of the observed inputs and outputs constructed by another system. Second, there are privacy concerns, as to train a surrogate model, one has to know the interactions from users other than the one who seeks an explanation. Third, such explanations may not be scrutable and actionable, as they typically return weights for items or other users that are difficult to comprehend, and hard to act upon so to improve the quality of one’s recommendations.