@inproceedings{zhang-etal-2026-efficient-framework,
    title = "An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision",
    author = "Zhang, Zishuai  and
      Yu, Sihao  and
      Xiewenyi  and
      Nie, Ying  and
      Wang, Junfeng  and
      Zheng, Zhiming  and
      Yin, Dawei  and
      Zhang, Hainan",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.67/",
    pages = "966--978",
    ISBN = "979-8-89176-394-4",
    abstract = "The whole-page reranking integrates retrieval results from multiple modalities and is critical for user experience of search engines, yet it requires costly large-scale expert annotations due to the complexity of assessing cross-modal relevances. In this paper, we propose SMAR, a novel whole-page reranking framework that converts single-modal rankers into page-level guidance by constructing budget-aware candidates for cross modal annotations and distilling intra-modality preferences to align relevance scales across modalities. Specifically, we use pre-trained single-modal rankers to construct candidate pages for limited cross-modal annotation at the page level. The whole-page reranker is then trained on these samples, enforcing consistency with single-modal preferences to preserve intra-modal ranking quality. Experiments on the Qilin and CrossRank datasets demonstrate that SMAR reduces annotation costs by 70-90{\%} while outperforming the fully-annotated reranking baselines. Further offline and online A/B tests confirm significant gains in both ranking metrics and user experience, validating the effectiveness and practical value of our approach in real-world search scenarios."
}