用梯度优化加速经典点击模型，支持大规模高效训练。
CLAX: Fast and Flexible Neural Click Models in JAX
- 用稳定梯度法替代传统EM算法优化点击模型。
- 单卡2小时处理百亿级用户会话数据，速度提升数个数量级。
- 模块化设计，适合研究者和工业界快速构建新模型。
CLAX 是一个基于 JAX 的开源库，实现了经典的点击模型并采用现代梯度优化方法。尽管过去十年神经点击模型不断发展，但基于概率图模型（PGMs）的复杂点击模型仍未系统采用梯度优化，导致从业者难以利用现代深度学习框架，同时失去经典模型的可解释性。CLAX 通过数值稳定的梯度优化方法，取代传统的 EM 算法，实现端到端可微分训练。其模块化设计支持嵌入、深度网络或自定义模块无缝集成到经典点击模型中。我们在包含超过十亿用户会话的完整 Baidu-ULTR 数据集上进行实验，仅用单张 GPU 在约 2 小时内完成训练，相较传统 EM 方法快数个数量级。CLAX 实现了十种经典点击模型，服务于希望理解用户行为、提升排序性能的工业实践者，以及开发新点击模型的研究人员。代码已开源：https://github.com/philipphager/clax。
原文摘要 · Abstract (English)
CLAX is a JAX-based library that implements classic click models using modern gradient-based optimization. While neural click models have emerged over the past decade, complex click models based on probabilistic graphical models (PGMs) have not systematically adopted gradient-based optimization, preventing practitioners from leveraging modern deep learning frameworks while preserving the interpretability of classic models. CLAX addresses this gap by replacing EM-based optimization with direct gradient-based optimization in a numerically stable manner. The framework's modular design enables the integration of any component, from embeddings and deep networks to custom modules, into classic click models for end-to-end optimization. We demonstrate CLAX's efficiency by running experiments on the full Baidu-ULTR dataset comprising over a billion user sessions in $\approx$ 2 hours on a single GPU, orders of magnitude faster than traditional EM approaches. CLAX implements ten classic click models, serving both industry practitioners seeking to understand user behavior and improve ranking performance at scale and researchers developing new click models. CLAX is available at: https://github.com/philipphager/clax
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