Counterfactual Explanations for Group Recommendations

Abstract

Recommendations provided by group recommendation systems are often complex and difficult for average users to comprehend. To address this issue, many existing approaches incorporate explanations alongside recommendations. However, as group recommendation models become increasingly sophisticated, offering clear and understandable explanations becomes more challenging. In response, we propose a system that delivers counterfactual, model-agnostic explanations. Counterfactual explanations focus exclusively on items with which the group has interacted, enhancing their interpretability. Additionally, model-agnostic explanations mitigate the intricacies of complex group recommendation models by treating them as black boxes, thereby simplifying the process and making the recommendations more accessible to users. We propose two heuristic approaches to produce fair group counterfactual explanations, i.e., explanations that consider all group members’ input and are not focused on individual group members. We conduct an evaluation of our methods utilizing the MovieLens dataset, highlighting our proposed methods’ effectiveness in generating counterfactual explanations for groups within a practical and efficient timeframe.