提出CIRR框架，让推荐系统在环境变化下仍能保持稳定并给出可验证的解释。
CIRR: Causal-Invariant Retrieval-Augmented Recommendation with Faithful Explanations under Distribution Shift
- 通过因果推断学习不变偏好表示，实现跨环境鲁棒性
- 在分布外场景下性能下降仅5.6%，远低于基线的15.4%
- 引入一致性约束，使解释与检索证据、推荐结果一致
近年来，检索增强生成（RAG）在推荐系统中引入外部知识方面展现出潜力。然而，现有RAG推荐系统面临两大挑战：(1) 在不同环境（如时间周期、用户群体）下对分布偏移敏感，导致分布外（OOD）场景性能下降；(2) 缺乏可验证的忠实解释。本文提出CIRR框架，通过因果推断学习环境不变的用户偏好表示，引导去偏的检索过程从多源信息中选择相关证据。此外，引入一致性约束，确保检索证据、生成解释与推荐输出之间的一致性。在两个真实数据集上的大量实验表明，CIRR在分布外场景下表现稳健，性能下降从基线的15.4%降至5.6%，同时解释忠实度提升26%，优于当前最优基线。
原文摘要 · Abstract (English)
Recent advances in retrieval-augmented generation (RAG) have shown promise in enhancing recommendation systems with external knowledge. However, existing RAG-based recommenders face two critical challenges: (1) vulnerability to distribution shifts across different environments (e.g., time periods, user segments), leading to performance degradation in out-of-distribution (OOD) scenarios, and (2) lack of faithful explanations that can be verified against retrieved evidence. In this paper, we propose CIRR, a Causal-Invariant Retrieval-Augmented Recommendation framework that addresses both challenges simultaneously. CIRR learns environment-invariant user preference representations through causal inference, which guide a debiased retrieval process to select relevant evidence from multiple sources. Furthermore, we introduce consistency constraints that enforce faithfulness between retrieved evidence, generated explanations, and recommendation outputs. Extensive experiments on two real-world datasets demonstrate that CIRR achieves robust performance under distribution shifts, reducing performance degradation from 15.4% (baseline) to only 5.6% in OOD scenarios, while providing more faithful and interpretable explanations (26% improvement in faithfulness score) compared to state-of-the-art baselines.
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