AI 中文总结
该研究针对社交搜索的语境差异等挑战，提出ASARL框架，结合多智能体数据整理与三阶段训练，在QQ搜索平台实验中提升了相关性指标、用户参与度及标注效率。
AI 中文摘要
在线社交平台的快速发展改变了交流与信息检索模式，催生了社交搜索，其中查询标题通常以非正式的、社区特有的语言表达。尽管大语言模型具备强大的通用语义理解能力，但其在社交搜索中的有效性受限于语境差异、数据稀缺性以及行为驱动的动态性。为应对这些挑战，我们提出自主社交感知相关性学习（Autonomous Social-Aware Relevance Learning, ASARL），这是一个整合多智能体数据整理与分阶段模型训练的全自动框架。ASARL利用协作智能体系统：ReasonAgent基于社交属性生成可解释的相关性标签，CriticAgent验证并确保逻辑一致性，GenAgent通过合成查询-标题对扩充长尾数据。基于整理后的数据集，ASARL采用三阶段训练：社交语境训练（Social Context Training, SCT）用于捕捉社交语言模式，偏好引导优化（Preference-Guided Optimization, PGO）使模型预测与行为信号对齐，社交蒸馏（Social Distillation, SD）将这些改进迁移至紧凑模型以实现高效部署。在QQ搜索平台开展的大量离线与在线实验表明，该方法在离线相关性指标、在线用户参与度指标上均实现显著提升，同时提高了标注效率。这些结果验证了在实际搜索系统中，将自主的、基于社交的数据治理与偏好对齐训练相结合的有效性。
英文摘要
The rapid growth of online social platforms has transformed communication and information retrieval, giving rise to social search, where queries-titles are typically expressed in informal, community-specific language. While large language models provide strong general-purpose semantic understanding, their effectiveness in social search is constrained by contextual discrepancy, data scarcity, and behavior-driven dynamics. To address these challenges, we propose the Autonomous Social-Aware Relevance Learning (ASARL), a fully automated framework that integrates multi-agent data curation with staged model training. ASARL leverages a collaborative agent system: ReasonAgent generates interpretable relevance labels grounded in social attributes, CriticAgent validates and ensures logical consistency, and GenAgent augments long-tail data through synthetic query-title pairs. Building on the curated dataset, ASARL employs three-stage training: Social Context Training (SCT) to capture social language patterns, Preference-Guided Optimization (PGO) to align model predictions with behavioral signals, and Social Distillation (SD) to transfer these improvements into compact models for efficient deployment. Extensive offline and online experiments on the QQ search platform demonstrate significant improvements in both offline relevance metrics and online user engagement indicators, along with enhanced annotation efficiency. These results validate the effectiveness of combining autonomous, socially grounded data governance with preference-aligned training in practical search systems.
