Paper-Conference

We develop a post-hoc, model-agnostic explanation mechanism for recommender systems. It returns counterfactual explanations defined as those minimal changes to the user’s interaction history that would result in the system not making the recommendation that is to be explained.

In recommender systems, fairness may concern either the consumers (end users, buyers, etc.) that receive recommendations, or the providers (producers, sellers, etc.) of the items being recommended. We have developed a common method that post-processes recommendations so as to ensure either consumer or provider fairness.