Off-Policy Evaluation for Semantic ID Recommenders: Does the Model's Own Code Hierarchy Help?

Title:Off-Policy Evaluation for Semantic ID Recommenders: Does the Model's Own Code Hierarchy Help?

Abstract:Generative recommenders increasingly emit semantic IDs (SIDs): each item is a short sequence of hierarchical discrete codes from a residual quantizer, decoded autoregressively. Before spending scarce A/B-test, a team may decide offline which decoder or reranking variants are worth testing - a job for off-policy evaluation (OPE). We ask a simple question: can the model's own SID tree serve as the action abstraction for that OPE? Our answer has three parts. (i) Under the near-argmax logging real recommenders use, per-item OPE is hopeless - as item-level effective sample size is usually small on production logs - but marginalizing items to code-prefix clusters restores estimable support and cuts error. (ii) This gain is thanks to coarsening, not to the hierarchy specifically; but the SID tree is what makes coarsening feasible in a generative system - each cluster's mass is exactly and cheaply returned by the decoder, whereas flat clustering requires enumerating item/leaf masses that a code-only decoder does not directly expose. (iii) Resolution depth is the operative knob - coarser under scarce support - and a conditional bias bound links the coarsening bias to the quantizer's worst-case reconstruction residual and the target-logging divergence.

Machine Learning (cs.LG)

arXiv:2608.28905 [cs.LG]

(or arXiv:2608.28905v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.28905 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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