先思后荐：释放序列推荐中的潜在推理能力

Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation

March 28, 2025

作者: Jiakai Tang, Sunhao Dai, Teng Shi, Jun Xu, Xu Chen, Wen Chen, Wu Jian, Yuning Jiang

cs.AI

摘要

序列推荐（SeqRec）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，在许多实际推荐系统中发挥着关键作用。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作为用户表示。我们认为，这种推理范式由于计算深度有限，难以建模用户偏好的复杂演变特性，且对长尾项目的理解不够细致，导致性能欠佳。为解决这一问题，我们提出了ReaRec，这是首个面向推荐系统的推理时计算框架，通过隐式多步推理增强用户表示。具体而言，ReaRec自回归地将序列的最后一个隐藏状态输入序列推荐器，同时引入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，我们提出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架构上的大量实验验证了ReaRec的通用性和有效性。值得注意的是，事后分析表明，ReaRec显著提升了多个序列推荐骨干模型的性能上限，提升幅度约为30%至50%。因此，我们相信这项工作为序列推荐的推理时计算研究开辟了一条崭新且充满前景的道路。

English

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 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.