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.

翻译：全页面重排在搜索引擎用户体验塑造中起着关键作用，它整合了来自多模态（如文档、图像、视频及大语言模型输出）的检索结果。现有方法主要依赖大规模人工标注数据，其获取成本高昂且耗时。这是因为全页面标注远比单模态标注复杂：它需要评估整个结果页面，同时考虑跨模态相关性差异。因此，如何在降低标注成本的同时提升全页面重排性能，仍是优化搜索引擎结果页面的核心挑战。本文提出SMAR，一种新颖的全页面重排框架，它利用强大的单模态排序器引导模态间相关性对齐以实现高效重排，仅需有限的全页面标注即可超越完全标注的重排模型。具体而言，首先在各模态专用数据上训练高质量单模态排序器。随后，针对每个查询，我们选取其输出的子集构建候选页面，并在页面级别进行人工标注。最后，利用这些有限标注训练全页面重排序器，并通过强制保持与单模态偏好的一致性来维持各模态内的排序质量。在Qilin与百度数据集上的实验表明，SMAR在实现显著排序性能提升的同时，将标注成本降低了约70-90%。在百度APP上进行的进一步离线与在线A/B测试也显示，其在标准排序指标及用户体验指标上均取得显著提升，充分验证了该方法在真实搜索场景中的有效性与实用价值。