Explaining Group Recommendations via Counterfactuals

TL;DR

This paper introduces a framework for explaining group recommendations using counterfactuals, revealing how removing specific interactions impacts group suggestions, and balances explanation quality with computational efficiency.

Contribution

It formalizes group counterfactual explanations, develops utility and fairness measures, and proposes heuristic algorithms for efficient, balanced explanations in group recommender systems.

Findings

Low-cost methods generate larger, less fair explanations.

Higher-cost methods produce concise, fairer explanations.

Pareto-filtering heuristic improves efficiency in sparse data settings.

Abstract

Group recommender systems help users make collective choices but often lack transparency, leaving group members uncertain about why items are suggested. Existing explanation methods focus on individuals, offering limited support for groups where multiple preferences interact. In this paper, we propose a framework for group counterfactual explanations, which reveal how removing specific past interactions would change a group recommendation. We formalize this concept, introduce utility and fairness measures tailored to groups, and design heuristic algorithms, such as Pareto-based filtering and grow-and-prune strategies, for efficient explanation discovery. Experiments on MovieLens and Amazon datasets show clear trade-offs: low-cost methods produce larger, less fair explanations, while other approaches yield concise and balanced results at higher cost. Furthermore, the Pareto-filtering heuristic demonstrates significant efficiency improvements in sparse settings.