2 个仓库
Awesome GitHub RepositoriesCTR Multi-Task Models
Multi-task learning architectures specifically designed for click-through rate prediction using shared-bottom, ESMM, MMOE, or PLE patterns.
Distinct from Multi-Task Learning Models: Distinct from Multi-Task Learning Models: focuses on CTR-specific multi-task architectures like ESMM and MMOE, not general sequence-based multi-task models.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · CTR Multi-Task Models. Refine with filters or upvote what's useful.
Awesome CTR Multi-Task Models GitHub Repositories
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- py-why/econml
py-why/EconML
4,683在 GitHub 上查看EconML 是一个 Python 因果推理库，旨在结合机器学习和计量经济学来估计异质处理效应。它作为计算条件平均处理效应的工具包，以确定特定干预措施如何影响个人或子群体。 该项目提供了一个用于双重机器学习和正交机器学习的框架，以从高维混杂因素中分离因果信号。它包括针对因果森林和工具变量学习者的专门实现，即使在存在未观察到的混杂因素的情况下，也允许恢复因果关系。 该库涵盖了广泛的功能，包括通过反驳测试和校准曲线进行的因果模型验证、个性化处理策略的构建以及动态处理机制的分析。它还支持用于不确定性量化的统计推理，以及使用基于树的模型和 Shapley 值对效应异质性的解释。 该项目主要通过 Jupyter Notebooks 实现和演示。
Fits a multi-task linear model with L1 regularization and sample weights to handle biased observational data.
Jupyter Notebookcausal-inferencecausalityeconometrics - shenweichen/deepctr-torch
shenweichen/DeepCTR-Torch
3,376在 GitHub 上查看DeepCTR-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
Train multiple prediction objectives simultaneously using shared-bottom, ESMM, MMOE, or PLE architectures.
Pythonctr-modelsdeep-learningdeepctr
