Generative recommendation (GR) has shown strong potential for sequential recommendation in an end-to-end generation paradigm. However, existing GR models suffer from severe cold-start collapse: their recommendation accuracy on cold-start items can drop to near zero. Current solutions typically rely on retraining with cold-start interactions, which is hindered by sparse feedback, high computational cost, and delayed updates, limiting practical utility in rapidly evolving recommendation catalogs. Inspired by model editing in NLP, which enables training-free knowledge injection into large language models, we explore how to bring this paradigm to generative recommendation. This, however, faces two key challenges: GR lacks the explicit subject-object binding common in natural language, making targeted edits difficult; and GR does not exhibit stable token co-occurrence patterns, making the injection of multi-token item representations unreliable. To address these challenges, we propose GenRecEdit, a model editing framework tailored for generative recommendation. GenRecEdit explicitly models the relationship between the full sequence context and next-token generation, adopts iterative token-level editing to inject multi-token item representations, and introduces a one-to-one trigger mechanism to reduce interference among multiple edits during inference. Extensive experiments on multiple datasets show that GenRecEdit substantially improves recommendation performance on cold-start items while preserving the model's original recommendation quality. Moreover, it achieves these gains using only about 9.5% of the training time required for retraining, enabling more efficient and frequent model updates.

翻译：生成式推荐（GR）在端到端生成范式下展现出强大的序列推荐潜力。然而，现有GR模型面临严重的冷启动崩溃问题：其对冷启动物品的推荐准确率可能降至接近零。当前解决方案通常依赖冷启动交互数据进行重新训练，但受限于稀疏的反馈信号、高昂的计算成本与延迟的更新频率，在快速演变的推荐目录中实用性受限。受自然语言处理中模型编辑技术的启发（该技术可实现无需训练的知识注入至大语言模型），我们探索如何将这一范式迁移至生成式推荐领域。但此举面临两大关键挑战：GR缺乏自然语言中显式的主谓宾绑定关系，导致定向编辑困难；且GR不具备稳定的词元共现模式，使得多词元物品表征的注入不可靠。为应对这些挑战，我们提出GenRecEdit——一个专为生成式推荐设计的模型编辑框架。GenRecEdit显式建模完整序列上下文与下一词元生成之间的关系，采用迭代式词元级编辑注入多词元物品表征，并引入一对一触发机制以减少推理过程中多重编辑间的干扰。跨多个数据集的广泛实验表明，GenRecEdit在显著提升冷启动物品推荐性能的同时，保持了模型原有的推荐质量。更关键的是，该方法仅需重训练所需训练时长的约9.5%即可实现这些增益，从而支持更高效、更频繁的模型更新。