解决生成式检索中语义标识随时间失效的问题，轻量更新不需重训。
Mitigating Collaborative Semantic ID Staleness in Generative Retrieval
- 基于新日志动态对齐旧语义标识，保持模型兼容性。
- 在三个数据集上高截断点召回率与nDCG均优于传统微调方法。
- 相比全量重训练，减少8-9倍计算开销，适合持续迭代场景。
生成式检索中的语义标识（SIDs）为每个项目分配离散标识，并将检索问题视为序列生成而非最近邻搜索。仅依赖内容的SIDs虽稳定，但忽略用户-项目交互模式；近期系统构建了考虑交互的SIDs，但随交互模式随时间漂移，这些标识会变得过时，即其协同语义不再匹配最新日志。以往工作通常假设微调期间SID词表固定，或把刷新视为需重训练的完整重建。然而，时间漂移下的SID过时现象极少被明确分析。为此，我们采用严格的时间顺序评估研究该问题，并提出一种轻量、模型无关的SID对齐更新方法。给定从近期日志推导出的新SID，我们将其对齐至现有SID词表，使检索器检查点保持兼容，从而可进行标准暖启动微调，无需完整重建-重训流程。在三个公开基准上，该方法在高截断点下持续提升Recall@K和nDCG@K，相较使用过时SID的朴素微调表现更优，且检索器训练计算量减少约8-9倍。
原文摘要 · Abstract (English)
Generative retrieval with Semantic IDs (SIDs) assigns each item a discrete identifier and treats retrieval as a sequence generation problem rather than a nearest-neighbor search. While content-only SIDs are stable, they do not take into account user-item interaction patterns, so recent systems construct interaction-informed SIDs. However, as interaction patterns drift over time, these identifiers become stale, i.e., their collaborative semantics no longer match recent logs. Prior work typically assumes a fixed SID vocabulary during fine-tuning, or treats SID refresh as a full rebuild that requires retraining. However, SID staleness under temporal drift is rarely analyzed explicitly. To bridge this gap, we study SID staleness under strict chronological evaluation and propose a lightweight, model-agnostic SID alignment update. Given refreshed SIDs derived from recent logs, we align them to the existing SID vocabulary so the retriever checkpoint remains compatible, enabling standard warm-start fine-tuning without a full rebuild-and-retrain pipeline. Across three public benchmarks, our update consistently improves Recall@K and nDCG@K at high cutoffs over naive fine-tuning with stale SIDs and reduces retriever-training compute by approximately 8-9 times compared to full retraining.
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