Joint Factual and Counterfactual Explanations for Top-k GNN-based Recommendations

Recently, graph neural networks (GNNs) have become the new state-of-the-art approach to developing powerful recommender systems. However, it is hard for GNN-based recommender systems to attach tangible explanations of why a specific item ends up in the list of top-k suggestions for a given user. Indeed, explaining GNN-based recommendations is unique, and existing GNN explanation methods are inappropriate since they are designed to explain node, edge, or graph classification rather than ranking. In this work, we propose GREASE, a novel method for explaining the list of top-k suggested items to a given user provided by any black-box GNN-based recommender system. Specifically, for each recommended item, GREASE first trains a surrogate GNN model on the subgraph obtained as the union of the target user-item pair and its l-hop neighborhood. Then, it jointly generates factual and counterfactual explanations by finding optimal adjacency matrix perturbations to capture the sufficient and necessary conditions for the item to be recommended. Experiments on real-world datasets show that GREASE can generate concise and compelling explanations for popular GNN-based recommender models.