用课程学习提升推荐解释的连贯性，让推荐更可信。
Curr-RLCER:Curriculum Reinforcement Learning For Coherence Explainable Recommendation
- 分阶段训练：从简单预测到复杂解释生成
- 奖励机制确保解释与评分一致，提升系统稳定性
- 适合关注推荐可解释性的研究者和工程师
可解释推荐系统旨在明确揭示每次推荐的原因，以增强系统的透明度和可信度。以往方法常联合预测评分并生成解释，却忽略了二者之间的不连贯性。为此，我们提出 Curr-RLCER，一种基于课程强化学习的连贯可解释推荐框架，具备动态评分对齐能力。该框架采用课程学习策略，逐步从基础预测（如点击率评分、选择性评分）过渡到开放式的推荐解释生成。每个阶段的奖励设计均旨在逐步提升系统的稳定性。此外，还提出一种以连贯性为导向的奖励机制，强制生成的解释与预测评分保持一致，并配有专门的评估方案。在三个可解释推荐数据集上的大量实验表明，所提框架有效。代码与数据集见：https://github.com/pxcstart/Curr-RLCER。
原文摘要 · Abstract (English)
Explainable recommendation systems (RSs) are designed to explicitly uncover the rationale of each recommendation, thereby enhancing the transparency and credibility of RSs. Previous methods often jointly predicted ratings and generated explanations, but overlooked the incoherence of such two objectives. To address this issue, we propose Curr-RLCER, a reinforcement learning framework for explanation coherent recommendation with dynamic rating alignment. It employs curriculum learning, transitioning from basic predictions (i.e., click through rating-CTR, selection-based rating) to open-ended recommendation explanation generation. In particular, the rewards of each stage are designed for progressively enhancing the stability of RSs. Furthermore, a coherence-driven reward mechanism is also proposed to enforce the coherence between generated explanations and predicted ratings, supported by a specifically designed evaluation scheme. The extensive experimental results on three explainable recommendation datasets indicate that the proposed framework is effective. Codes and datasets are available at https://github.com/pxcstart/Curr-RLCER.
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