从Top-1到Top-K：推荐系统反事实解释的可重复性研究与基准测试
From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
AI总结：
本文系统地重现并评估了11种最先进的推荐系统反事实解释方法，探讨了解释格式、评估层级和扰动范围对解释效果的影响，揭示了不同方法在有效性与稀疏性之间的权衡及大规模图结构下的可扩展性限制。
摘要：
反事实解释（CEs）通过识别最小的用户-物品交互修改来理解推荐系统，但现有方法在不同协议、数据集、推荐器和指标下评估，影响了可重复性和公平比较。本文系统地重现、重新实现并重新评估了11种最先进的推荐系统CE方法，涵盖原生解释器（如LIME-RS、SHAP、PRINCE、ACCENT、LXR、GREASE）和专为GNN设计的图基解释器。本文提出统一的基准测试框架，从解释格式（隐式 vs 显式）、评估层级（物品级 vs 列表级）和扰动范围（用户交互向量 vs 用户-物品交互图）三个维度评估解释器。评估协议包括有效性、稀疏性和计算复杂度指标，并扩展了现有物品级评估到Top-K列表级解释。通过在三个真实世界数据集和六个代表性推荐模型上的广泛实验，分析了先前报告的CE方法在不同设置下的泛化能力。发现有效性与稀疏性之间的权衡强烈依赖于具体方法和评估设置，尤其是在显式格式下；此外，解释器性能在物品级和列表级评估中基本一致，而一些基于图的解释器在大规模推荐图上表现出显著的可扩展性限制。本文结果修正并挑战了关于CE生成方法在推荐系统中鲁棒性和实用性的早期结论：https://github.com/L2R-UET/CFExpRec.
英文摘要：
Counterfactual explanations (CEs) provide an intuitive way to understand recommender systems by identifying minimal modifications to user-item interactions that alter recommendation outcomes. Existing CE methods for recommender systems, however, have been evaluated under heterogeneous protocols, using different datasets, recommenders, metrics, and even explanation formats, which hampers reproducibility and fair comparison. Our paper systematically reproduces, re-implement, and re-evaluate eleven state-of-the-art CE methods for recommender systems, covering both native explainers (e.g., LIME-RS, SHAP, PRINCE, ACCENT, LXR, GREASE) and specific graph-based explainers originally proposed for GNNs. Here, a unified benchmarking framework is proposed to assess explainers along three dimensions: explanation format (implicit vs. explicit), evaluation level (item-level vs. list-level), and perturbation scope (user interaction vectors vs. user-item interaction graphs). Our evaluation protocol includes effectiveness, sparsity, and computational complexity metrics, and extends existing item-level assessments to top-K list-level explanations. Through extensive experiments on three real-world datasets and six representative recommender models, we analyze how well previously reported strengths of CE methods generalize across diverse setups. We observe that the trade-off between effectiveness and sparsity depends strongly on the specific method and evaluation setting, particularly under the explicit format; in addition, explainer performance remains largely consistent across item level and list level evaluations, and several graph-based explainers exhibit notable scalability limitations on large recommender graphs. Our results refine and challenge earlier conclusions about the robustness and practicality of CE generation methods in recommender systems: https://github.com/L2R-UET/CFExpRec.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
