用语义扩展提升广告匹配精度，兼顾覆盖与相关性。
Improving Ad matching via Cluster-Adaptive Keyword Expansion and Relevance tuning
- 通过语言模型扩展关键词语义，不改变用户查询
- 集群自适应阈值保持匹配精度，降低无关匹配
- 增量学习优化相关性模型，适合动态广告场景
在搜索广告中，关键词匹配将用户查询与相关广告关联。基于词元的匹配虽扩大了广告覆盖面，但易因过度宽松的语义扩展降低相关性。本文通过文档侧语义扩展提升关键词覆盖范围，利用预训练孪生模型生成广告关键词的稠密向量表示，并通过最近邻搜索识别语义相关变体。为保持精度，提出基于聚类的阈值机制，根据局部语义密度动态调整相似度阈值。每个扩展关键词映射至一组卖家商品，可能与原始意图部分偏离。为确保相关性，采用轻量级决策树集成的增量学习策略，对下游相关性模型进行适配。该系统在提升相关性和点击率（CTR）的同时，具备可扩展、低延迟特性，能适应查询行为与广告库存的持续变化。
原文摘要 · Abstract (English)
In search advertising, keyword matching connects user queries with relevant ads. While token-based matching increases ad coverage, it can reduce relevance due to overly permissive semantic expansion. This work extends keyword reach through document-side semantic keyword expansion, using a language model to broaden token-level matching without altering queries. We propose a solution using a pre-trained siamese model to generate dense vector representations of ad keywords and identify semantically related variants through nearest neighbor search. To maintain precision, we introduce a cluster-based thresholding mechanism that adjusts similarity cutoffs based on local semantic density. Each expanded keyword maps to a group of seller-listed items, which may only partially align with the original intent. To ensure relevance, we enhance the downstream relevance model by adapting it to the expanded keyword space using an incremental learning strategy with a lightweight decision tree ensemble. This system improves both relevance and click-through rate (CTR), offering a scalable, low-latency solution adaptable to evolving query behavior and advertising inventory.
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