Scaling a Search Conversion Rate (CVR) prediction model, especially in high-traffic environments, presents a challenge: superior model quality needs to be balanced with strict constraints on training cost and serving latency. This paper details an effective approach for scaling modern search CVR prediction models. We begin with an empirical study to understand the scaling performance of search CVR models, analyzing how quality improves as we scale three key factors of model backbone computation, the size of embedding parameters, and the volume of training data. We use a large-scale production dataset, comprising over a year of customer interaction logs from a high-traffic e-commerce platform, to evaluate the scalability of several state-of-the-art architectures and their ensembles. Our key findings are: (1) selecting the right backbone and scaling factors is crucial; (2) the impact of scaling backbone, embedding, and data is largely independent and additive, which has implications for more efficient scaling exploration; (3) a streamlined warmstart strategy can accelerate training iterations while simplifying new updates; (4) inference optimization strategies such as decoupled graph execution and dynamic batching can enable low-latency GPU serving even for high-capacity models. Compared to a baseline of a pre-scaling production model, we ultimately deployed a model trained on 2.5x larger training data with 8x more inference compute while having minimal latency impact. Online A/B tests also demonstrate that our launches achieved a combined +2.6% gain in a key metric of search conversion rate.

翻译：扩展搜索转化率（CVR）预测模型（尤其是在高流量环境中）面临一项挑战：需要兼顾卓越的模型质量与训练成本和服务时延的严格约束。本文详述了扩展现代搜索CVR预测模型的有效方法。我们首先通过实证研究理解搜索CVR模型的规模化性能，分析随着模型主干计算量、嵌入参数规模及训练数据量三个关键因素的扩大，质量如何提升。我们采用包含高流量电商平台一年多客户交互日志的大规模生产数据集，评估了多种最先进架构及其集成模型的扩展性。主要发现如下：（1）选择合适的主干网络与扩展因子至关重要；（2）主干部、嵌入层与数据的扩展效应在很大程度上相互独立且可叠加，这对更高效的扩展探索具有指导意义；（3）精简的热启动策略可在简化新更新流程的同时加速训练迭代；（4）解耦图执行与动态批处理等推理优化策略，能够为高容量模型实现低时延GPU服务。相较于未扩展前的基线生产模型，我们最终部署的模型在训练数据量扩大2.5倍、推理计算量提升8倍的同时，对时延影响极小。在线A/B测试也表明，我们的发布使搜索转化率这一关键指标整体提升了+2.6%。