CLAX: Fast and Flexible Neural Click Models in JAX
Philipp Hager, Onno Zoeter, Maarten de Rijke

TL;DR
CLAX is a JAX-based library that enables fast, flexible, and gradient-based optimization of classic and neural click models, significantly improving efficiency and modularity for large-scale user behavior analysis.
Contribution
It introduces a novel gradient-based optimization approach for probabilistic graphical models in click modeling, replacing EM, with a modular design for easy integration of components.
Findings
Achieves full dataset processing in approximately 2 hours on a single GPU.
Implements ten classic click models with improved efficiency.
Enables end-to-end optimization of complex click models.
Abstract
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 2 hours on a single…
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Taxonomy
TopicsData Visualization and Analytics · Multimodal Machine Learning Applications · Gaussian Processes and Bayesian Inference
