Sequential Recommendation (SeqRec) aims to predict the next item by capturing sequential patterns from users' historical interactions, playing a crucial role in many real-world recommender systems. However, existing approaches predominantly adopt a direct forward computation paradigm, where the final hidden state of the sequence encoder serves as the user representation. We argue that this inference paradigm, due to its limited computational depth, struggles to model the complex evolving nature of user preferences and lacks a nuanced understanding of long-tail items, leading to suboptimal performance. To address this issue, we propose \textbf{ReaRec}, the first inference-time computing framework for recommender systems, which enhances user representations through implicit multi-step reasoning. Specifically, ReaRec autoregressively feeds the sequence's last hidden state into the sequential recommender while incorporating special reasoning position embeddings to decouple the original item encoding space from the multi-step reasoning space. Moreover, we introduce two lightweight reasoning-based learning methods, Ensemble Reasoning Learning (ERL) and Progressive Reasoning Learning (PRL), to further effectively exploit ReaRec's reasoning potential. Extensive experiments on five public real-world datasets and different SeqRec architectures demonstrate the generality and effectiveness of our proposed ReaRec. Remarkably, post-hoc analyses reveal that ReaRec significantly elevates the performance ceiling of multiple sequential recommendation backbones by approximately 30\%-50\%. Thus, we believe this work can open a new and promising avenue for future research in inference-time computing for sequential recommendation.

翻译：序列推荐旨在通过捕捉用户历史交互中的序列模式来预测下一个项目，在许多现实世界的推荐系统中扮演着关键角色。然而，现有方法主要采用直接前向计算范式，其中序列编码器的最终隐藏状态被用作用户表示。我们认为，这种推理范式由于其有限的计算深度，难以对用户偏好的复杂演化性质进行建模，并且缺乏对长尾项目的细致理解，导致性能欠佳。为解决此问题，我们提出了 \textbf{ReaRec}，这是首个用于推荐系统的推理时计算框架，它通过隐式多步推理来增强用户表示。具体而言，ReaRec 自回归地将序列的最后一个隐藏状态馈送到序列推荐器中，同时结合特殊的推理位置嵌入，以将原始项目编码空间与多步推理空间解耦。此外，我们引入了两种轻量级的基于推理的学习方法——集成推理学习（ERL）和渐进推理学习（PRL），以进一步有效挖掘 ReaRec 的推理潜力。在五个公开真实世界数据集和不同 SeqRec 架构上进行的大量实验证明了我们提出的 ReaRec 的通用性和有效性。值得注意的是，事后分析表明，ReaRec 将多个序列推荐骨干模型的性能上限显著提升了约 30\%-50\%。因此，我们相信这项工作能为序列推荐的推理时计算未来研究开辟一条崭新且充满前景的道路。