Author

Michael M. Müller

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Open access Aug 2026

Measuring What Matters: Consistency and Compactness in Evaluation of Counterfactual Explanations

Explainability in recommender systems (RS) remains a pivotal challenge. Counterfactual explanations have emerged as a particularly actionable paradigm, offering intuitive “what-if” reasoning. However, their evaluation lacks principled standards. Current metrics primarily assess whether explanations change the top-ranke...

Amir Reza Mohammadi, Andreas Peintner, Michael M. Müller et al. · ACM Transactions on Recommen... · 0 citations

Conference Open access Sep 2026

Beyond Top-1: Addressing Inconsistencies in Evaluating Counterfactual Explanations for Recommender Systems (Extended Abstract)

Counterfactual explanations have become an important paradigm for improving the transparency of machine learning models by showing how small input changes can alter model outputs. While substantial progress has been made in generating such explanations, their evaluation remains insufficiently standardized, particularly...

Amir Reza Mohammadi, Andreas Peintner, Michael M. Müller et al. · Proceedings of the Thirty-Fi... · 6 citations