Conventional sequential recommendation models have achieved remarkable success in mining implicit behavioral patterns. However, these architectures remain structurally blind to explicit user intent: they struggle to adapt when a user's immediate goal (e.g., expressed via a natural language prompt) deviates from their historical habits. While Large Language Models (LLMs) offer the semantic reasoning to interpret such intent, existing integration paradigms force a dilemma: LLM-as-a-recommender paradigm sacrifices the efficiency and collaborative precision of ID-based retrieval, while Reranking methods are inherently bottlenecked by the recall capabilities of the underlying model. In this paper, we propose Decoupled Promptable Sequential Recommendation (DPR), a model-agnostic framework that empowers conventional sequential backbones to natively support Promptable Recommendation, the ability to dynamically steer the retrieval process using natural language without abandoning collaborative signals. DPR modulates the latent user representation directly within the retrieval space. To achieve this, we introduce a Fusion module to align the collaborative and semantic signals, a Mixture-of-Experts (MoE) architecture that disentangles the conflicting gradients from positive and negative steering, and a three-stage training strategy that progressively aligns the semantic space of prompts with the collaborative space. Extensive experiments on real-world datasets demonstrate that DPR significantly outperforms state-of-the-art baselines in prompt-guided tasks while maintaining competitive performance in standard sequential recommendation scenarios.

翻译：传统序列推荐模型在挖掘隐式行为模式方面取得了显著成功。然而，这些架构在结构上对显式用户意图仍存在盲区：当用户的即时目标（例如通过自然语言提示表达）偏离其历史习惯时，模型难以适应。尽管大型语言模型（LLMs）具备解析此类意图的语义推理能力，但现有集成范式面临两难困境：LLM-as-a-recommender 范式牺牲了基于ID检索的效率和协同精度，而重排序方法则受限于底层模型的召回能力瓶颈。本文提出解耦可提示序列推荐（DPR），这是一个模型无关的框架，能够赋能传统序列骨干网络原生支持可提示推荐——即在不放弃协同信号的前提下，利用自然语言动态引导检索过程的能力。DPR 直接在检索空间中对潜在用户表征进行调制。为实现这一目标，我们引入了融合模块以对齐协同信号与语义信号，采用混合专家（MoE）架构以解耦正向与负向引导产生的冲突梯度，并设计了三阶段训练策略以逐步对齐提示语义空间与协同空间。在真实数据集上的大量实验表明，DPR 在提示引导任务中显著优于当前最先进的基线方法，同时在标准序列推荐场景中保持竞争优势。