Accurate explicit and implicit product identification in search queries is critical for enhancing user experiences, especially at a company like Adobe which has over 50 products and covers queries across hundreds of tools. In this work, we present a novel approach to training a product classifier from user behavioral data. Our semantic model led to >25% relative improvement in CTR (click through rate) across the deployed surfaces; a >50% decrease in null rate; a 2x increase in the app cards surfaced, which helps drive product visibility.

翻译：在搜索查询中准确识别显式与隐式产品，对于提升用户体验至关重要，尤其是在Adobe这类拥有超过50种产品且覆盖数百种工具查询的公司。本研究提出了一种基于用户行为数据训练产品分类器的新方法。我们的语义模型使部署界面的点击率（CTR）相对提升了25%以上，空值率下降了超过50%，应用卡片展现量增加了2倍，从而显著提高了产品可见性。