Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversion rates, and other optimization events. To support a wide variety of product surfaces and advertiser goals, these platforms frequently maintain an extensive ecosystem of machine learning (ML) models. However, operating at this scale creates significant development and efficiency challenges. Substantial engineering effort is required to regularly refresh ML models and propagate new techniques, which results in long latencies when deploying ML innovations across the ecosystem. We present a large-scale empirical study comparing model performance, efficiency, and ML technique propagation between a standardized model-building approach and independent per-model optimization in recommendation systems. To facilitate this standardization, we propose the Standard Model Template (SMT) -- a framework that generates high-performance models adaptable to diverse data distributions and optimization events. By utilizing standardized, composable ML model components, SMT reduces technique propagation complexity from $O(n \cdot 2^k)$ to $O(n + k)$ where $n$ is the number of models and $k$ the number of techniques. Evaluating an extensive suite of models over four global development cycles within Meta's production ads ranking ecosystem, our results demonstrate: (1) a 0.63% average improvement in cross-entropy at neutral serving capacity, (2) a 92% reduction in per-model iteration engineering time, and (3) a $6.3\times$ increase in technique-model pair adoption throughput. These findings challenge the conventional wisdom that diverse optimization goals inherently require diversified ML model design.

翻译：现代计算广告平台通常依赖推荐系统来预测用户响应，例如点击率、转化率及其他优化事件。为支持多样化的产品界面和广告商目标，这些平台往往维护着庞大的机器学习模型生态。然而，如此规模的运营带来了显著的开发与效率挑战。定期刷新机器学习模型并推广新技术需要大量的工程投入，这导致在整个生态系统中部署机器学习创新时出现长延迟。我们提出了一项大规模实证研究，比较了推荐系统中标准化模型构建方法与独立逐模型优化在模型性能、效率和机器学习技术传播方面的差异。为促进标准化，我们提出了标准模型模板（SMT）——一种能够生成适应多样化数据分布和优化事件的高性能模型的框架。通过利用标准化、可组合的机器学习模型组件，SMT将技术传播复杂度从$O(n \cdot 2^k)$降低至$O(n + k)$，其中$n$为模型数量，$k$为技术数量。我们在Meta生产广告排序生态系统中对四个全球开发周期的广泛模型套件进行了评估，结果表明：（1）在中性服务容量下交叉熵平均改进0.63%；（2）逐模型迭代工程时间减少92%；（3）技术-模型对采用吞吐量提升6.3倍。这些发现挑战了“多样化优化目标必然需要多样化机器学习模型设计”的传统观点。