AI 中文总结
研究针对序列推荐中推理过程构建问题，提出无强化学习框架RecRec，通过解耦推理与预测克服单状态瓶颈，由上下文压缩器和递归推理器组成，分两阶段训练，在多数据集上优于现有方法，揭示推理状态结构可进一步探索。
AI 中文摘要
序列推荐系统依靠单次前向传递来编码用户交互历史并预测下一个项目。最近在序列推荐中探索了通过潜在推理增加推理时计算，在最终预测前逐步进行模型推理，取得了有前景的结果。然而，如何构建序列推荐的推理过程仍是开放问题。现有方法在单个d维状态中耦合推理和预测，限制了推理深度且常依赖强化学习的多阶段管道。我们提出RecRec，一个无强化学习框架，将推理与预测解耦，克服了先前方法固定d维状态瓶颈。RecRec由上下文压缩器和递归推理器组成，分两个简单监督阶段训练。上下文压缩器将骨干网络的隐藏状态提炼为一小部分潜在兴趣，兴趣多样性正则化器鼓励每个兴趣捕捉用户行为的不同方面。递归推理器然后在单独的中间潜在空间中推理来细化这些兴趣。深度监督使推理深度在推理时无需重新训练即可自由调整。在四个真实世界数据集上，RecRec优于现有推理增强方法，在四个数据集中的三个上，增益超过训练时的深度。我们的发现指出了一种解耦的多向量方法，从先前方法的单状态瓶颈中释放潜在推理，表明推理状态结构是序列推荐中进一步探索的设计轴。
英文摘要
Sequential recommender systems rely on a single forward pass to encode user interaction histories and predict the next item. Increasing inference-time computation through latent reasoning, with the model proceeding step by step before the final prediction, has been recently explored in sequential recommendation with promising results. However, how to structure the reasoning process for sequential recommendation remains an open question. Existing approaches couple reasoning and prediction in a single $d$-dimensional state, limiting reasoning depth and often relying on multi-stage pipelines with reinforcement learning (RL). We propose RecRec (Recursive Reasoning for Recommendation), an RL-free framework that decouples reasoning from prediction, overcoming the fixed $d$-dimensional state bottleneck of prior methods. RecRec consists of a Context Compressor and a Recursive Reasoner, trained in two simple supervised stages. The Context Compressor distills the backbone's hidden states into a small set of latent interests, with an Interest Diversity Regularizer encouraging each interest to capture a distinct aspect of user behavior. The Recursive Reasoner then refines these interests by reasoning in a separate intermediate latent space. Deep supervision lets the reasoning depth be freely adjusted at inference without retraining. On four real-world datasets, RecRec outperforms state-of-the-art reasoning-enhanced methods, and on three of four datasets, gains extend past the training-time depth. Our findings point to a decoupled, multi-vector recipe that unleashes latent reasoning from the single-state bottleneck of prior methods, suggesting reasoning-state structure as a design axis to explore further in sequential recommendation.
CommentsAccepted at RecSys 2026, 10 pages
