标题： 基于单模态监督的全页重新排序高效框架 显示英文标题

标题： An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision

摘要： 全页面重新排序在塑造搜索引擎的用户体验中起着关键作用，它整合了来自多种模态的检索结果，如文档、图像、视频和大语言模型输出。 现有方法主要依赖于大规模的人工标注数据，获取成本高且耗时。 这是因为全页面标注比单一模态复杂得多：它需要评估整个结果页面，同时考虑跨模态的相关性差异。 因此，在减少标注成本的同时提高全页面重新排序性能，仍然是优化搜索引擎结果页面（SERP）的一个关键挑战。 在本文中，我们提出了SMAR，一种新颖的全页面重新排序框架，该框架利用强大的单模态排序器来指导模态间相关性对齐，以实现有效的重新排序，仅使用有限的全页面标注即可超越完全标注的重新排序模型。 具体来说，首先在各自模态特定的数据上训练高质量的单模态排序器。 然后，对于每个查询，我们选择其输出的一个子集来构建候选页面，并在页面级别进行人工标注。 最后，我们使用这些有限的标注来训练全页面重新排序器，并通过与单模态偏好保持一致性来维持每个模态内的排名质量。 在Qilin和百度数据集上的实验表明，SMAR将标注成本降低了约70-90%，同时相比基线实现了显著的排名提升。 在百度APP上的进一步离线和在线A/B测试也显示了标准排名指标以及用户体验指标的显著提升，充分验证了我们的方法在真实搜索场景中的有效性和实用价值。 显示英文摘要

摘要： The whole-page reranking plays a critical role in shaping the user experience of search engines, which integrates retrieval results from multiple modalities, such as documents, images, videos, and LLM outputs. Existing methods mainly rely on large-scale human-annotated data, which is costly to obtain and time-consuming. This is because whole-page annotation is far more complex than single-modal: it requires assessing the entire result page while accounting for cross-modal relevance differences. Thus, how to improve whole-page reranking performance while reducing annotation costs is still a key challenge in optimizing search engine result pages(SERP). In this paper, we propose SMAR, a novel whole-page reranking framework that leverages strong Single-modal rankers to guide Modal-wise relevance Alignment for effective Reranking, using only limited whole-page annotation to outperform fully-annotated reranking models. Specifically, high-quality single-modal rankers are first trained on data specific to their respective modalities. Then, for each query, we select a subset of their outputs to construct candidate pages and perform human annotation at the page level. Finally, we train the whole-page reranker using these limited annotations and enforcing consistency with single-modal preferences to maintain ranking quality within each modality. Experiments on the Qilin and Baidu datasets demonstrate that SMAR reduces annotation costs by about 70-90\% while achieving significant ranking improvements compared to baselines. Further offline and online A/B testing on Baidu APPs also shows notable gains in standard ranking metrics as well as user experience indicators, fully validating the effectiveness and practical value of our approach in real-world search scenarios.