发表机构
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究生成式推荐中语义ID，发现其虽有粗略组织但丢失精细结构，精确令牌不由项目含义单独决定。提出轻量级推理时方法项目支持解码，无需额外参数或重新训练，实验表明该方法能改进NDCG@10，增益高达31.2%。
AI 中文摘要
语义ID（SIDs）是生成式推荐的核心组成部分。当前基于SID的系统为同一令牌序列赋予三个角色。共享前缀用于组织相关项目，完整的SID标识单个项目，每个生成的令牌缩小仍可返回的项目范围。我们系统地研究了从项目编码、SID构建到自回归生成和最终推荐的SID。研究了SID构建如何改变项目表示以及这些变化如何影响生成。在三个亚马逊领域和八种SID构建中，SID邻域平均仅能找回编码器十个最近邻中的32.2%。替代项目描述在99.57%的受控案例中仍能首先检索到相应项目，但会改变38.4%的精确SID。这些结果表明，SID保留了广泛的组织，但失去了编码器的许多精细局部结构，其精确令牌并非仅由项目含义决定。在生成过程中这种损失变得很重要。在最终语义令牌之后，TIGER在SID过滤前作为合理推荐的保留目标中仅保留29.9%。基于这些发现，我们提出了项目支持解码（ISD），这是一种轻量级推理时方法，允许在束搜索丢弃之前进行特定用户的项目排名以支持相应的SID前缀。然后相同的排名对生成的项目进行排序。ISD不需要额外参数或对SID构造函数或解码器进行重新训练。我们通过实验表明，在每个评估设置中，ISD相对于相应SID主干在NDCG@10上有所改进，相对增益高达31.2%。我们的结果表明，SID提供了有用的粗略项目组织，但其精细边界不应单独决定生成过程中哪些项目仍然可用。
英文摘要
Semantic IDs (SIDs) are now a central component of generative recommendation. Current SID-based systems assign three roles to the same token sequence. Shared prefixes are intended to organize related items, the complete SID identifies an individual item, and each generated token narrows the items that can still be returned. We systematically investigate SIDs from item encoding and SID construction to autoregressive generation and final recommendation. We examine how SID construction changes item representations and how those changes affect generation. Across three Amazon domains and eight SID constructions, SID neighborhoods recover only 32.2% of the encoder's ten nearest neighbors on average. Alternative item descriptions still retrieve the corresponding item first in 99.57% of controlled cases, yet change 38.4% of exact SIDs. These results show that SIDs retain broad organization but lose much of the encoder's fine local structure, while their exact tokens are not determined by item meaning alone. This loss becomes consequential during generation. After the final semantic token, TIGER retains only 29.9% of held-out targets that were plausible recommendations before SID filtering. Motivated by these findings, we propose Item-Supported Decoding (ISD), a lightweight inference-time method that allows a user-specific item ranking to support corresponding SID prefixes before beam search discards them. The same ranking then orders the generated items. ISD requires no additional parameters or retraining of the SID constructor or decoder. We empirically show that ISD improves NDCG@10 over the corresponding SID backbone in every evaluated setting, with relative gains of up to 31.2%. Our results show that SIDs provide useful coarse item organization, but their fine boundaries should not alone determine which items remain available during generation.
