arXiv Machine Learning
Off-Policy Evaluation for Semantic ID Recommenders: Does the Model's Own Code Hierarchy Help?
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SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation
SIDScope is a diagnostic tool that evaluates Semantic-ID mappings used between item tokenizers and generative recommenders. It normalizes artifacts, verifies provenance, profiles mapping structure, compares revisions, and tracks path-to-item outcomes in generated traces. Using data from Amazon and Yelp, SIDScope shows that interface health depends on multiple signals, revealing gaps in prefix alignment, trace accounting, and refresh handling that affect model reuse.
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SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation
SIDScope is a diagnostic tool that evaluates Semantic‑ID interfaces used in generative recommendation systems. It normalizes item‑to‑code artifacts, verifies provenance, profiles mapping structure, and compares revisions while tracking path‑to‑item outcomes in generated traces. Using nine tokenizer exports from Amazon and Yelp data, SIDScope shows that interface health depends on multiple signals and reveals gaps in prefix alignment, trace accounting, and mapping refresh effects.