Fidelity-Aware Recommendation Explanations via Stochastic Path Integration
Explanation fidelity, which measures how accurately an explanation reflects a model's true reasoning, remains critically underexplored in recommender systems. We introduce SPINRec (Stochastic Path Integration for Neural Recommender Explanations), a model-agnostic approach that adapts path-integration techniques to the sparse and implicit nature of recommendation data. To overcome the limitations of prior methods, SPINRec employs stochastic baseline sampling: instead of integrating from a fixed or unrealistic baseline, it samples multiple plausible user profiles from the empirical data distribution and selects the most faithful attribution path. This design captures the influence of both observed and unobserved interactions, yielding more stable and personalized explanations. We conduct the most comprehensive fidelity evaluation to date across three models (MF, VAE, NCF), three datasets (ML1M, Yahoo! Music, Pinterest), and a suite of counterfactual metrics, including AUC-based perturbation curves and fixed-length diagnostics. SPINRec consistently outperforms all baselines, establishing a new benchmark for faithful explainability in recommendation. Code and evaluation tools are publicly available at https://github.com/DeltaLabTLV/SPINRec.
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Oren Barkan
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Yahlly Schein
admin_assigned
2025-11-25T09:35:29.071Z
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Yahlly Schein
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Yahlly21
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Yehonatan Elisha
admin_assigned
2025-11-25T09:34:58.953Z
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Yehonatan Elisha
Yoniel
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Yoniel
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Veronika Bogina
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Mikhail Baklanov
admin_assigned
2025-11-25T09:35:17.142Z
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Mikhail Baklanov
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mikhailbaklanov
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Noam Koenigstein
admin_assigned
2025-11-25T09:35:09.571Z
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Noam Koenigstein
deltalab-noam
user
deltalab-noam
Fidelity-Aware Recommendation Explanations via Stochastic Path Integration
Explanation fidelity, which measures how accurately an explanation reflects a model's true reasoning, remains critically underexplored in recommender systems. We introduce SPINRec (Stochastic Path Integration for Neural Recommender Explanations), a model-agnostic approach that adapts path-integration techniques to the sparse and implicit nature of recommendation data. To overcome the limitations of prior methods, SPINRec employs stochastic baseline sampling: instead of integrating from a fixed or unrealistic baseline, it samples multiple plausible user profiles from the empirical data distribution and selects the most faithful attribution path. This design captures the influence of both observed and unobserved interactions, yielding more stable and personalized explanations. We conduct the most comprehensive fidelity evaluation to date across three models (MF, VAE, NCF), three datasets (ML1M, Yahoo! Music, Pinterest), and a suite of counterfactual metrics, including AUC-based perturbation curves and fixed-length diagnostics. SPINRec consistently outperforms all baselines, establishing a new benchmark for faithful explainability in recommendation. Code and evaluation tools are publicly available at https://github.com/DeltaLabTLV/SPINRec.
SPINRec, a model-agnostic approach, enhances explanation fidelity in recommender systems by using stochastic baseline sampling and path-integration techniques to capture both observed and unobserved interactions.
explanation fidelity path-integration stochastic baseline sampling user profiles empirical data distribution attribution path counterfactual metrics AUC-based perturbation curves fixed-length diagnostics MF VAE NCF ML1M Yahoo! Music Pinterest