发表机构
Renmin University of China; Alibaba（中国人民大学; 阿里巴巴）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于语义ID生成的生成式推荐对冷门商品的覆盖情况，采用绝对时间时间协议及多种分析方法，发现当前模型能覆盖部分未来商品，但在未见原子令牌和无支持路径上有困难，揭示其组合但非完全开放式特点并给出未来方向。
AI 中文摘要
基于语义ID的生成式推荐将商品表示为共享语义令牌序列，实现了超越孤立商品ID的令牌重组。然而，封闭世界的重组并不一定意味着时间上的开放令牌冷启动归纳，即新商品以未见的原子令牌或支持不足的SID路径进入商品目录。在这项工作中，我们在绝对时间时间协议下重新审视基于SID的生成式推荐，该协议分离可见和不可见目标，并在令牌级别诊断冷门商品可达性。通过可见/不可见命中分析、冷度分类法和预言前缀探测，我们表明当前基于SID的模型偶尔可以覆盖由观察到的令牌和前缀支持的未来商品，但在未见的原子令牌和无支持的SID路径上存在困难。我们通过将SID生成解释为分层语义分桶来进一步解释这一边界：早期令牌选择粗略的语义区域，而后期令牌细化特定商品的路径。这些发现表明，SID生成是组合式的，但不是完全开放式的，并为更独立的SID空间、基于评分的接口和动态文本上下文提供了未来方向。
英文摘要
Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs. However, closed-world recombination does not necessarily imply temporal open-token cold-start induction, where new items enter the item catalog with unseen atomic tokens or weakly supported SID paths. In this work, we revisit SID-based generative recommendation under an absolute-time temporal protocol that separates seen and unseen targets and diagnoses the cold item reachability at the token level. Through seen/unseen-hit analysis, coldness taxonomy, and oracle-prefix probing, we show that current SID-based models can occasionally reach future items supported by observed tokens and prefixes, but struggle with unseen atomic tokens and unsupported SID paths. We further explain this boundary by interpreting SID generation as hierarchical semantic bucketing: early tokens select coarse semantic regions, while later tokens refine item-specific paths. These findings show that SID generation is compositional but not fully open-ended, and suggest future directions in more independent SID spaces, scoring-based interfaces, and dynamic textual context.
