A unified benchmark of eleven CE methods shows effectiveness-sparsity trade-offs vary by method and format, performance is consistent from item to list level, and graph-based explainers face scalability limits on large graphs.
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Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.
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From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
A unified benchmark of eleven CE methods shows effectiveness-sparsity trade-offs vary by method and format, performance is consistent from item to list level, and graph-based explainers face scalability limits on large graphs.
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Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
Heterogeneous graph neural networks with post-hoc explanations improve accuracy on six land-use indicators from mobility data and provide feature attribution and counterfactual insights aligned with commuting patterns.