用因果推理让推荐理由更可信，避免被热门商品误导。
Counterfactual Language Reasoning for Explainable Recommendation Systems
- 构建因果图，确保解释因素先于推荐结果产生
- 在多个数据集上，推荐准确率和解释合理性均优于基线
- 特别解决热门商品带来的偏好偏差，适合想做可解释推荐的研究者
可解释推荐系统通过透明推理提升用户信任与决策质量。现有方法通常将推荐生成与解释创建分离，违背了因果优先原则——解释因素应逻辑上先于结果。本文提出一种新框架，将结构化因果模型与大语言模型结合，实现推荐流程的因果一致性。通过构建因果图并进行反事实调整，强制解释因素作为推荐预测的因果前提。特别针对商品流行度造成的混淆效应，开发去偏机制以分离真实用户偏好与从众偏差。在多个推荐场景的综合实验中，CausalX 在推荐准确率、解释合理性及偏见缓解方面均显著优于基线模型。
原文摘要 · Abstract (English)
Explainable recommendation systems leverage transparent reasoning to foster user trust and improve decision-making processes. Current approaches typically decouple recommendation generation from explanation creation, violating causal precedence principles where explanatory factors should logically precede outcomes. This paper introduces a novel framework integrating structural causal models with large language models to establish causal consistency in recommendation pipelines. Our methodology enforces explanation factors as causal antecedents to recommendation predictions through causal graph construction and counterfactual adjustment. We particularly address the confounding effect of item popularity that distorts personalization signals in explanations, developing a debiasing mechanism that disentangles genuine user preferences from conformity bias. Through comprehensive experiments across multiple recommendation scenarios, we demonstrate that CausalX achieves superior performance in recommendation accuracy, explanation plausibility, and bias mitigation compared to baselines.
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