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[2602.18929] Give Users the Wheel: Towards Promptable Recommendation Paradigm
Summary
This paper introduces the Decoupled Promptable Sequential Recommendation (DPR) framework, which enhances traditional recommendation systems by integrating user intent through natural language prompts, improving retrieval efficiency and accuracy.
Why It Matters
As recommendation systems become increasingly central to user experience across platforms, understanding and adapting to user intent is crucial. This research addresses the limitations of existing models by proposing a novel approach that leverages Large Language Models (LLMs) to enhance user interaction and recommendation relevance.
Key Takeaways
- DPR framework enables dynamic user intent integration into recommendation systems.
- The model maintains collaborative signals while allowing for natural language prompts.
- Extensive experiments show DPR outperforms existing state-of-the-art methods.
- The approach is model-agnostic, making it applicable across various recommendation architectures.
- A three-stage training strategy aligns semantic and collaborative spaces effectively.
Computer Science > Information Retrieval arXiv:2602.18929 (cs) [Submitted on 21 Feb 2026] Title:Give Users the Wheel: Towards Promptable Recommendation Paradigm Authors:Fuyuan Lyu, Chenglin Luo, Qiyuan Zhang, Yupeng Hou, Haolun Wu, Xing Tang, Xue Liu, Jin L.C. Guo, Xiuqiang He View a PDF of the paper titled Give Users the Wheel: Towards Promptable Recommendation Paradigm, by Fuyuan Lyu and 8 other authors View PDF HTML (experimental) Abstract: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 representat...