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Awesome GitHub RepositoriesClick-Through Rate Multi-Task Models
Frameworks for training models that predict multiple objectives like click and conversion rates using shared representations.
Distinct from Multi-Task Learning Models: Distinct from Multi-Task Learning Models: specifically targets CTR prediction with shared-bottom, ESMM, MMOE, and PLE architectures.
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Awesome Click-Through Rate Multi-Task Models GitHub Repositories
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- shenweichen/deepctr-torch
shenweichen/DeepCTR-Torch
3,376Ver en GitHubDeepCTR-Torch is a deep learning library for building click-through rate prediction models. It provides a modular framework for assembling custom prediction architectures from pre-built core, interaction, and sequence layers, enabling the construction of deep neural networks that estimate click probability from user behavior data. The library specializes in feature interaction modeling, offering components for learning low-order, high-order, and adaptive-order feature crosses. It supports multi-task learning for predicting multiple objectives simultaneously, such as click and conversion rates
Framework for training models that predict multiple objectives like click and conversion rates using shared representations.
Pythonctr-modelsdeep-learningdeepctr
