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Interpretable explanations for recommender systems and other machine learningmodels are crucial to gain user trust. Prior works that have focused on pathsconnecting users and items in a heterogeneous network have several limitations,such as discovering relationships rather than true explanations, ordisregarding other users' privacy. In this work, we take a fresh perspective,and present PRINCE: a provider-side mechanism to produce tangible explanationsfor end-users, where an explanation is defined to be a set of minimal actionsperformed by the user that, if removed, changes the recommendation to adifferent item. Given a recommendation, PRINCE uses a polynomial-time optimalalgorithm for finding this minimal set of a user's actions from an exponentialsearch space, based on random walks over dynamic graphs. Experiments on tworeal-world datasets show that PRINCE provides more compact explanations thanintuitive baselines, and insights from a crowdsourced user-study demonstratethe viability of such action-based explanations. We thus posit that PRINCEproduces scrutable, actionable, and concise explanations, owing to its use ofcounterfactual evidence, a user's own actions, and minimal sets, respectively.

PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems