SIDScope诊断生成推荐中语义ID接口的健康状态，揭示其多信号特性。
SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation
- 通过溯源分析接口映射结构与生成路径，验证其一致性与可复现性。
- 发现前缀对齐在检索阶段重要，但评分独立后作用减弱，影响推荐效果。
- 适合评估推荐系统接口质量，尤其关注模型复用前的验证需求。
语义ID映射是物品分词器与生成式推荐系统之间的可复用接口，但现有映射很少说明其一致性、结构暴露程度、生成路径解析情况，或刷新后需重新验证的内容。SIDScope是一个源追溯诊断工具，用于支持这些决策。它统一物品到编码的产物，验证来源与合并关系，剖析映射结构，比较成对版本，并追踪生成轨迹中路径到物品的对应结果。基于来自Amazon和Yelp数据的七类分词器导出的九个源追溯导出物——八条可执行路径加一条可审计快照——研究发现接口健康度是多信号而非单一指标。核心发现为机制依赖型：当检索依赖SID前缀时，前缀对齐显著关联未见候选曝光；但评分过程脱离前缀后，该相关性减弱。训练轨迹追踪揭示第二个隐藏缺口：有效目标路径可在不唯一召回目标物品的情况下仍保留1.2-3.0个百分点的存活率。一次刷新案例表明：修复映射本身无法恢复继承的生成器；模型复用需额外的手动交接检查。该工具包提供冻结证据摘要、合规报告、轨迹标签、表格构建器及仅需CPU的验证器，支持对产物就绪状态、接口风险及模型复用前再验证的判断。
原文摘要 · Abstract (English)
Semantic-ID mappings are reusable interfaces between item tokenizers and generative recommenders, yet released mappings rarely state whether they are coherent, what structure they expose, how generated paths resolve, or what must be revalidated after a refresh. SIDScope is a source-traced diagnostic resource for these decisions. It normalizes item-to-code artifacts, verifies provenance and joins, profiles mapping structure, compares paired revisions, and accounts for path-to-item outcomes in generated traces. Across nine source-traced tokenizer exports from seven families on Amazon and Yelp data - eight executable routes plus one auditable snapshot - SIDScope reveals that interface health is multi-signal rather than scalar. Its central finding is mechanism-conditional: prefix alignment strongly tracks held-out candidate exposure when retrieval consumes SID prefixes, then weakens as scoring becomes prefix-independent. Trained trace accounting exposes a second hidden gap: a valid target path can survive without uniquely retrieving the target item by 1.2-3.0 percentage points. A refresh case establishes a third: repairing the mapping does not by itself restore an inherited generator; model reuse requires a separate handoff check. The package provides frozen evidence summaries, conformance reports, trace labels, table builders, and CPU-only verifiers. It supports decisions about artifact readiness, interface risks, and revalidation before model reuse.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。