Meta titles and descriptions strongly shape engagement in search and recommendation platforms, yet optimizing them remains challenging. Search engine ranking models are black box environments, explicit labels are unavailable, and feedback such as click-through rate (CTR) arrives only post-deployment. Existing template, LLM, and retrieval-augmented approaches either lack diversity, hallucinate attributes, or ignore whether candidate phrasing has historically succeeded in ranking. This leaves a gap in directly leveraging implicit signals from observable outcomes. We introduce MetaSynth, a multi-agent retrieval-augmented generation framework that learns from implicit search feedback. MetaSynth builds an exemplar library from top-ranked results, generates candidate snippets conditioned on both product content and exemplars, and iteratively refines outputs via evaluator-generator loops that enforce relevance, promotional strength, and compliance. On both proprietary e-commerce data and the Amazon Reviews corpus, MetaSynth outperforms strong baselines across NDCG, MRR, and rank metrics. Large-scale A/B tests further demonstrate 10.26% CTR and 7.51% clicks. Beyond metadata, this work contributes a general paradigm for optimizing content in black-box systems using implicit signals.

翻译：元标题和描述在搜索与推荐平台中对用户参与度具有重要影响，但其优化仍具挑战性。搜索引擎排序模型属于黑盒环境，缺乏显式标注，且点击率等反馈仅在部署后产生。现有模板方法、大语言模型及检索增强方法存在多样性不足、属性虚构或忽略候选表述历史排序表现等问题，导致难以直接利用可观测结果中的隐式信号。本文提出MetaSynth——一种基于隐式搜索反馈的多智能体检索增强生成框架。该框架从排名靠前的结果构建范例库，基于产品内容与范例生成候选摘要，并通过评估-生成循环迭代优化输出，确保相关性、推广强度与合规性。在专有电商数据与亚马逊评论语料上的实验表明，MetaSynth在NDCG、MRR及排序指标上均优于现有基线方法。大规模A/B测试进一步验证了其点击率提升10.26%、点击量增长7.51%的效果。除元数据优化外，本研究为利用隐式信号优化黑盒系统内容提供了通用范式。