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ICEGR: An Intent-Coherent End-to-End Generative Retrieval Framework for E-commerce Search — arXiv:2608.29652

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29652
License (as archived): CC0-1.0
Archived: 2026-10-17T09:44:16.940Z
SHA-256 of the archived text: 5b2414f146c182f566f93af37bd735b5742aed39f888f52ab180c6b032945660
Stable URL of this page: https://metraipolis.com/documento/dc_ccdbae399ca5

How to cite

ICEGR: An Intent-Coherent End-to-End Generative Retrieval Framework for E-commerce Search — arXiv:2608.29652 — arXiv (en), https://arxiv.org/abs/2608.29652 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ccdbae399ca5 (Metraipolis archive dc_ccdbae399ca5, sha256 5b2414f146c182f566f93af37bd735b5742aed39f888f52ab180c6b032945660)

BibTeX

@misc{dcccdbae399ca5,
  title = {ICEGR: An Intent-Coherent End-to-End Generative Retrieval Framework for E-commerce Search — arXiv:2608.29652},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29652},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ccdbae399ca5; sha256 5b2414f146c182f566f93af37bd735b5742aed39f888f52ab180c6b032945660},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29652. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generative Retrieval (GR) is promising for e-commerce search, yet existing methods struggle to maintain query-intent consistency throughout the training pipeline. First, semantic ID (SID) construction based on static product information limits the ability of SIDs to encode product-intent associations. Second, although supervised fine-tuning (SFT) learns product-SID mappings across the catalog, low-exposure products still lack real query-intent supervision because query-to-SID training relies solely on online logs, resulting in poor retrieval performance for these products. Third, business-oriented preference optimization may favor popular or high-value products over those that best match the query intent, weakening query-product relevance. To address these issues, we propose ICEGR, an Intent-Coherent End-to-End Generative Retrieval Framework for E-commerce Search that integrates query intent consistently throughout the GR training pipeline. ICEGR comprises three components: (1) Intent-Aware SID Construction incorporates query-intent signals into SID construction, enabling SIDs to capture search intent beyond static product information; (2) Synthetic Query-Enhanced Unified SFT unifies multiple SFT tasks under the query-to-SID objective and augments sparse supervision from online logs with synthetic queries, providing complementary query-intent supervision for low-exposure products; and (3) Relevance-Calibrated Preference Optimization integrates query-product relevance and business signals into a margin-adaptive preference objective, preserving query intent while enabling business preference learning. Offline results show that ICEGR improves Recall@20 by 21.7% and NDCG@20 by 26.6% over the baseline. Deployed as an end-to-end generative retrieval pathway in Baidu E-commerce Search, ICEGR achieves relative improvements of 3.52% in CTR, 15.96% in order volume, and 7.53% in GMV in an A/B test.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in cs.IR).
Categorie arXiv: Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2608.29652 — 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 2026-10-17T09:12:53.472Z dalla pagina https://arxiv.org/catchup/cs.IR/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).