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DEGR: Dual Exploration-Driven Generative Re-Ranking for Adaptive Cross-Request Context Bridging — arXiv:2608.04809

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.04809
License (as archived): CC0-1.0
Archived: 2026-10-31T11:47:39.193Z
SHA-256 of the archived text: e625dbe07e29f513ffd8a257036f73aaaf266efc6fe336f291faa34bd004e310
Stable URL of this page: https://metraipolis.com/documento/dc_e4591ade91e6

How to cite

DEGR: Dual Exploration-Driven Generative Re-Ranking for Adaptive Cross-Request Context Bridging — arXiv:2608.04809 — arXiv (en), https://arxiv.org/abs/2608.04809 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e4591ade91e6 (Metraipolis archive dc_e4591ade91e6, sha256 e625dbe07e29f513ffd8a257036f73aaaf266efc6fe336f291faa34bd004e310)

BibTeX

@misc{dce4591ade91e6,
  title = {DEGR: Dual Exploration-Driven Generative Re-Ranking for Adaptive Cross-Request Context Bridging — arXiv:2608.04809},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.04809},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e4591ade91e6; sha256 e625dbe07e29f513ffd8a257036f73aaaf266efc6fe336f291faa34bd004e310},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.04809. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In industrial recommendation systems, the re-ranking stage balances business objectives and diversity for sequence-level optimization while modeling contextual information. However, constrained by fixed upstream supply, existing methods fail to deliver further effectiveness gains, especially under low-quality supply. To overcome this, re-ranking can actively balance immediate and exploratory value, for instance, by prioritizing exploratory exposure under low-quality supply to preserve browsing potential and facilitate serendipitous conversions. Therefore, we propose a Dual Exploration-Driven Generative Re-Ranking (DEGR) method. DEGR adopts a hybrid supervised-reinforcement exploration and optimization paradigm, guided by an exploratory reward model that adaptively balances immediate and exploratory value. The hybrid optimization paradigm integrates three key components: supervised learning, exploration diversity constraint, and adaptive reward-weighted ORPO for preference optimization. Through this dual exploration, the generator ultimately acts as an adaptive cross-request contextual bridge. Offline and online experiments indicate that DEGR outperforms SOTA methods, achieving improvements of up to 1.22% UCTR and 0.20% PV in the JD E-commerce recommendation system.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-06 in cs.IR).
Categorie arXiv: Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2608.04809 — 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 2026-10-31T11:37:02.909Z dalla pagina https://arxiv.org/catchup/cs.IR/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).