arXiv 2026 / Recommender Systems

SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation

A 2026 arXiv resource paper for auditing Semantic-ID interfaces in generative recommendation before downstream training, refresh, or model reuse.

arXiv DOI Full publications Code

Research area

User reactivation, generative recommendation, Semantic-ID interface diagnostics, dynamic retrieval, graph embedding, counterfactual watch-time prediction, and efficient CTR modeling form the main technical line.

Publication record

Authors
J Ding, H Qin, T Wu, Y Cao
Venue
arXiv preprint arXiv:2608.18779
Year
2026
Area
Recommender Systems

Problem

Semantic-ID mappings are reusable interfaces between item tokenizers and generative recommenders, but released mappings often omit whether they are coherent, traceable, or safe to reuse after refresh.

Approach

SIDScope normalizes item-to-code artifacts, verifies provenance and joins, profiles mapping structure, compares paired revisions, and accounts for generated path-to-item outcomes.

Findings

The resource shows that interface health is multi-signal rather than scalar; prefix alignment matters when retrieval consumes prefixes, and mapping repair does not automatically validate reuse of an inherited generator.

Takeaway

The work turns Semantic-ID artifacts into auditable interfaces, supporting decisions about readiness, interface risk, and revalidation before model reuse.