Fidelity-Aware Recommendation Explanations via Stochastic Path Integration

TL;DR

SPINRec introduces a stochastic path integration method for recommendation explanations, improving fidelity by sampling plausible user profiles to produce more stable, personalized, and accurate explanations across multiple models and datasets.

Contribution

The paper presents SPINRec, a novel model-agnostic explanation method that enhances fidelity in recommender systems through stochastic baseline sampling and path integration.

Findings

SPINRec outperforms existing baselines in fidelity metrics.

It provides more stable and personalized explanations.

Achieves state-of-the-art results across multiple datasets and models.

Abstract

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.