Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical interactions, making modern recommender systems structurally similar to large language models (LLMs). As privacy and safety concerns grow, these systems increasingly require concept unlearning to remove sensitive or harmful concepts associated with items. However, existing LLM unlearning methods cannot be directly applied to generative recommendation. Unlike word tokens with explicit semantics, SIDs are abstract identifiers that are often shared by both forget and retain items, leading to severe conflicts between concept removal and recommendation utility preservation. To address this challenge, we propose TRACER, an end-to-end concept unlearning framework based on token reassignment. Rather than directly suppressing shared SIDs, TRACER reassigns concept-related items to alternative tokens that better facilitate forgetting while minimizing side effects on retained items. We further introduce a coherence regularizer to preserve semantic consistency among retain items during unlearning. Experiments on real-world recommendation datasets demonstrate that TRACER effectively removes target concepts while substantially better preserving recommendation utility than existing unlearning baselines.

翻译：生成式推荐将下一项预测建模为用户历史交互语义ID序列的自回归生成，使现代推荐系统在结构上与大型语言模型（LLMs）相似。随着隐私与安全问题的日益凸显，此类系统亟需通过概念遗忘机制移除与项目相关的敏感或有害概念。然而，现有LLM遗忘方法无法直接应用于生成式推荐。与具有显式语义的词汇令牌不同，语义ID是抽象标识符，常被遗忘项与保留项共享，导致概念移除与推荐性能保持之间存在严重冲突。为解决此问题，我们提出TRACER——一种基于令牌重新分配的端到端概念遗忘框架。不同于直接抑制共享语义ID，TRACER将概念相关项重新分配至更利于遗忘的替代令牌，同时最小化对保留项的负面影响。我们进一步引入一致性正则化项，在遗忘过程中保持保留项的语义一致性。在真实推荐数据集上的实验表明，TRACER能有效移除目标概念，同时比现有遗忘基线方法更好地保持推荐性能。