Sequential recommendation (SR) is traditionally formulated as next-item prediction over chronological item interactions. Although recent generative recommendation (GR) methods introduce new machinery, such as semantic IDs, autoregressive decoding, and unified token spaces, they largely inherit the same item-only modeling assumption. We argue that this design constitutes a structural bottleneck, because user decision-making is not purely behavioral: while item interactions reveal what users choose, review feedback often explains why they choose it by exposing latent evaluative factors. Motivated by this observation, we propose Review-Augmented Generative Recommendation (RAGR), a novel GR framework that incorporates review feedback into the generative user sequence rather than treating reviews as auxiliary side information. Specifically, RAGR introduces a Review-Augmented User Sequence Modeling mechanism that interleaves item semantic IDs and review semantic IDs in chronological order to construct a mixed behavioral-semantic sequence, enabling review signals to participate directly in autoregressive next-token generation. To preserve the recommendation objective, we further introduce an Item-Centric Task Generation Alignment strategy based on direct preference optimization (DPO), encouraging the model to favor item tokens over review tokens at prediction positions. Experiments on three real-world datasets show that RAGR yields consistent and significant gains over strong GR backbones. Our code is available at https://github.com/Zhang-Yingyi/RAGR.

翻译：序列推荐（SR）传统上被定义为基于时序物品交互的下一个物品预测。尽管近期生成式推荐（GR）方法引入了语义ID、自回归解码与统一token空间等新机制，但其本质上仍继承了仅基于物品交互的建模假设。我们认为这种设计构成了结构性瓶颈，因为用户决策并非纯粹的行为驱动：虽然物品交互揭示了用户的选择结果，但评论反馈往往通过暴露潜在评价因子，解释了用户为何做出该选择。基于此观察，我们提出评论增强的生成式推荐（RAGR），这是一种新颖的GR框架，它并非将评论视为辅助侧信息，而是将其直接融入生成式用户序列。具体而言，RAGR引入了评论增强的用户序列建模机制，该机制按时间顺序交替排列物品语义ID与评论语义ID，构建混合行为语义序列，使评论信号能够直接参与自回归的下一个token生成。为维持推荐目标，我们进一步提出基于直接偏好优化（DPO）的物品中心任务生成对齐策略，鼓励模型在预测位置优先选择物品token而非评论token。在三个真实世界数据集上的实验表明，RAGR在强GR基线上取得了一致且显著的性能提升。我们的代码已开源：https://github.com/Zhang-Yingyi/RAGR。