让推荐模型秒变听话，用自然语言指令实时调整推荐结果。
CRAMER: Control via Request-Aware Masking for Editing Recommenders
- 通过请求感知掩码控制冻结模型参数，无需重训或大模型推理。
- 在多个数据集上超越4个顶尖基线，性能提升且开销极低。
- 适合需要快速响应用户即时需求的推荐系统场景。
序列推荐模型虽强大，却难以响应用户的即时请求，导致无法及时适应用户兴趣变化。现有方法或需重训整个主干网络，或依赖大语言模型的推理能力（即提示工程），计算开销高，难以应用于大规模推荐服务。本文提出控制通过请求感知掩码编辑推荐模型（CRAMER），利用用户自然语言请求即时改变序列推荐模型的行为。受模型控制理论启发，CRAMER将用户请求视为控制信号，通过掩码机制调节冻结的主干参数，实现对多样化请求的瞬时适配，避免昂贵的再训练。在多个大规模基准数据集上的实验表明，CRAMER在多项推荐指标上优于四个最先进基线，同时保持极低开销。此外，该框架展现出更强的可控性与跨领域适应能力，为请求感知的序列推荐建立新范式。
原文摘要 · Abstract (English)
Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt their recommendations to the user's timely interests. Unfortunately, existing user request adaptation methods often incur high computational overhead due to either 1) retraining the entire backbone network or 2) leveraging the inference ability of large language models (a.k.a. prompt engineering), limiting their applicability in large-scale recommendation services. This paper presents Control via Request-Aware Masking for Editing Recommenders (CRAMER), a framework that takes users' natural-language requests to immediately change sequential recommendation models' behavior. Specifically, inspired by the model control theory, CRAMER treats user requests as control signals to modulate frozen backbone parameters through masking, achieving instant adaptation to diverse requests while avoiding costly retraining. Experiments on multiple large-scale benchmark datasets show that CRAMER outperforms four state-of-the-art request-aware baselines across multiple recommendation metrics while achieving minimal overhead. Moreover, the proposed framework exhibits enhanced controllability and cross-domain adaptability, establishing a new paradigm for request-aware sequential recommendation.
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