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SIDRec: End-to-End Semantic ID Representation and Modeling for Recommendation — arXiv:2611.01136

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01136
License (as archived): CC0-1.0
Archived: 2026-11-03T10:56:28.061Z
SHA-256 of the archived text: 08d19320aae1c3654a366d8dc950c6df1c8af2045923d80fdfd7a9abba429b27
Stable URL of this page: https://metraipolis.com/documento/dc_c759d84fefda

How to cite

SIDRec: End-to-End Semantic ID Representation and Modeling for Recommendation — arXiv:2611.01136 — arXiv (en), https://arxiv.org/abs/2611.01136 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c759d84fefda (Metraipolis archive dc_c759d84fefda, sha256 08d19320aae1c3654a366d8dc950c6df1c8af2045923d80fdfd7a9abba429b27)

BibTeX

@misc{dcc759d84fefda,
  title = {SIDRec: End-to-End Semantic ID Representation and Modeling for Recommendation — arXiv:2611.01136},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01136},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c759d84fefda; sha256 08d19320aae1c3654a366d8dc950c6df1c8af2045923d80fdfd7a9abba429b27},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01136. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Semantic IDs (SIDs) have emerged as a promising approach for discriminative recommendation by encoding continuous item rep- resentations into compact discrete identifiers. However, existing SID-based methods face two fundamental limitations: semantic mis- match between content semantics and collaborative preferences, and objective mismatch between SID construction and downstream recommendation optimization. To address these challenges, we propose SIDRec, an end-to-end recommendation-aware SID learn- ing framework that jointly models content and collaborative in- formation while aligning SID construction with recommendation objectives. Specifically, SIDRec introduces a self-updating fusion mechanism that explicitly integrates pretrained content represen- tations with collaborative embeddings initialized from a trained recommendation model, enabling preference-aware semantic mod- eling. Furthermore, SIDRec employs an MMoE-inspired joint opti- mization mechanism with shared experts and task-specific gates, allowing recommendation supervision to directly guide differen- tiable quantization and produce task-adaptive discrete representa- tions. Extensive experiments on three public benchmark datasets demonstrate that SIDRec consistently outperforms all competing methods across both AUC and GAUC, achieving up to 5.15% relative improvements over the strongest baselines. Further ablation stud- ies validate the effectiveness and complementarity of collaborative semantic modeling and end-to-end SID optimization.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01136 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:49:38.530Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).