From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
Researchers unified evaluation of eleven counterfactual explanation methods for recommender systems, addressing fragmentation across datasets, metrics, and protocols that previously blocked fair comparison. The benchmarking framework assesses explainers across three dimensions, covering both native methods like LIME-RS and SHAP plus graph neural network approaches.
MentionsLIME-RS · SHAP · PRINCE · ACCENT · LXR · GREASE
Read full story at arXiv cs.LG →(arxiv.org)
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