Faithful Evaluation of Semantic-ID Tokenizers for Generative Recommendation

[2605.25330] Faithful Evaluation of Semantic-ID Tokenizers for Generative Recommendation Skip to main content Search Submit Donate Log in Computer Science > Information Retrieval arXiv:2605.25330 (cs) [Submitted on 25 May 2026 ( v1 ), last revised 22 Aug 2026 (this version, v2)] Title: Faithful Evaluation of Semantic-ID Tokenizers for Generative Recommendation Authors: Qian Zhang , Lech Szymanski , Haibo Zhang , Jeremiah D. Deng View PDF HTML (experimental) Abstract: Generative recommendation based on Semantic IDs (SIDs) represents each item as a discrete sequence of SIDs and is conventionally evaluated by matching the generated SID sequence against the target item's SID sequence. This evaluation is faithful only when each SID sequence uniquely identifies one item. In practice, SID collisions violate this condition: across the evaluated SID tokenizers and datasets, collision rates reach 30.52%, and SID-level Hit@10 is inflated by up to 103.36% relative to item-level Hit@10. To address this evaluation gap, we introduce Collision-Corrected Evaluation (CCE), which defines collision-aware item-level metrics (ItemHit@K, ItemNDCG@K) computed from generated SID sequences, and Zero-Collision Reassignment (ZCR), which constructs zero-collision SID assignments for existing tokenizers via minimum-cost reassignment. Applying these methods to four datasets and five representative SID tokenizers, we find that metric inflation scales with collision rate and is large enough to flip pairwise tokenizer comparisons under item-level re-evaluation. This finding calls into question the reliability of SID-level rankings reported in prior work and indicates that faithful tokenizer evaluation requires item-level correction or zero-collision SID assignments. Comments: Accepted at CIKM 2026 (Full Research Paper). Code: this https URL Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2605.25330 [cs.IR] (or arXiv:2605.25330v2 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2605.25330 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM '26), 2026 Related DOI : https://doi.org/10.1145/3799682.3841124 Focus to learn more DOI(s) linking to related resources Submission history From: Qian Zhang [ view email ] [v1] Mon, 25 May 2026 01:22:45 UTC (697 KB) [v2] Sat, 22 Aug 2026 05:31:03 UTC (712 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.IR < prev | next > new | recent | 2026-05 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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