TRACER: 面向生成式推荐中概念擦除的令牌重分配
TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
AI总结：
针对生成式推荐中概念遗忘与推荐效用冲突的问题，提出基于令牌重分配的概念遗忘框架TRACER，通过将概念相关物品重分配给替代令牌并引入一致性正则化，有效移除目标概念同时保持推荐效用。
摘要：
生成式推荐将下一项预测形式化为基于用户历史交互导出的语义ID（SID）序列的自回归生成，使得现代推荐系统在结构上类似于大型语言模型（LLM）。随着隐私和安全问题的增加，这些系统越来越需要概念遗忘来移除与物品相关的敏感或有害概念。然而，现有的LLM遗忘方法不能直接应用于生成式推荐。与具有明确语义的词令牌不同，SID是抽象标识符，通常被遗忘和保留物品共享，导致概念移除和推荐效用保持之间的严重冲突。为了解决这一挑战，我们提出了TRACER，一种基于令牌重分配的端到端概念遗忘框架。TRACER不是直接抑制共享的SID，而是将概念相关物品重分配给能够更好地促进遗忘同时最小化对保留物品的副作用的替代令牌。我们进一步引入了一致性正则化器，以在遗忘过程中保持保留物品之间的语义一致性。在真实世界推荐数据集上的实验表明，TRACER有效地移除了目标概念，同时比现有的遗忘基线更好地保持了推荐效用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
