Explaining Group Recommendations via Counterfactuals

2026-01-23

AI Summary

This work addresses the lack of transparency in existing group recommendation systems, which often repurpose individual recommendation methods and fail to account for the interactive nature of group preferences, making it difficult to explain why specific items are recommended to a group. The paper formally introduces the concept of group counterfactual explanations and proposes a counterfactual-based explanation framework that generates explanations by analyzing how removing historical interactions affects recommendations. It further incorporates group-oriented utility and fairness metrics. To enhance efficiency, the authors design heuristic strategies—including Pareto filtering and a grow-and-prune approach—to generate concise explanations effectively, even in sparse interaction scenarios. Experiments on MovieLens and Amazon datasets demonstrate that the method successfully balances explanation conciseness, fairness, and computational cost, with Pareto filtering notably improving efficiency in sparse settings.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

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.

Problem

• group recommender systems
• explanation
• counterfactuals
• transparency
• group preferences

Innovation

• counterfactual explanations
• group recommender systems
• fairness-aware recommendation
• Pareto-based filtering
• explainable AI