At Expedia, learning-to-rank (LTR) models plays a key role on our website in sorting and presenting information more relevant to users, such as search filters, property rooms, amenities, and images. A major challenge in deploying these models is ensuring consistent feature scaling between training and production data, as discrepancies can lead to unreliable rankings when deployed. Normalization techniques like feature standardization and batch normalization could address these issues but are impractical in production due to latency impacts and the difficulty of distributed real-time inference. To address consistent feature scaling issue, we introduce a scale-invariant LTR framework which combines a deep and a wide neural network to mathematically guarantee scale-invariance in the model at both training and prediction time. We evaluate our framework in simulated real-world scenarios with injected feature scale issues by perturbing the test set at prediction time, and show that even with inconsistent train-test scaling, using framework achieves better performance than without.

翻译：在Expedia，学习排序（LTR）模型在我们的网站上发挥着关键作用，用于对用户更相关的信息（如搜索过滤器、酒店客房、设施和图片）进行排序和展示。部署这些模型时面临的一个主要挑战是确保训练数据与生产数据之间特征尺度的一致性，因为尺度差异可能导致模型部署后产生不可靠的排序结果。特征标准化和批量归一化等归一化技术可以解决这些问题，但由于延迟影响和分布式实时推理的难度，这些方法在生产环境中并不实用。为解决特征尺度一致性问题，我们提出了一种尺度不变LTR框架，该框架结合了深度神经网络和宽度神经网络，从数学上保证了模型在训练和预测阶段的尺度不变性。我们通过在预测时扰动测试集注入特征尺度问题，在模拟真实场景中评估了该框架，结果表明即使训练与测试尺度不一致，使用该框架仍能获得优于未使用框架的性能表现。