shenweichen/DeepCTR-Torch

Features

• Click-Through Rate Predictors - Builds and trains deep neural networks to predict ad or item click probability from user behavior data.
• CTR Model Builders - Modular deep learning toolkit for assembling custom CTR prediction architectures from core, interaction, and sequence layers.
• Feature Interaction Models - Captures complex relationships between features using specialized neural network layers for higher prediction accuracy.
• Feature Interaction Learning Toolkits - Library providing pre-built layers for learning low-order, high-order, and adaptive feature crosses in prediction models.
• Low-Order Interaction Layers - Capture pairwise feature relationships through vector product operations between embeddings.
• Expert Routing Gates - Implements learned gating functions that route inputs to shared expert modules per task in multi-task CTR models.
• Deep Click Model Builders - Construct a deep neural network for click-through rate prediction using modular building blocks.
• Custom Neural Architectures - Combine core neural network layers to construct bespoke prediction architectures.
• Multi-Task Learning Models - Trains a single model to predict multiple related outcomes simultaneously, such as click and conversion rates, using shared representations.
• Click-Through Rate Multi-Task Models - Framework for training models that predict multiple objectives like click and conversion rates using shared representations.
• CTR Multi-Task Models - Train multiple prediction objectives simultaneously using shared-bottom, ESMM, MMOE, or PLE architectures.
• Gated Expert Routing Layers - Use separate gating networks per task to selectively combine outputs from shared expert modules.
• Shared-Bottom Architectures - Shares bottom layers across multiple prediction objectives to learn click and conversion rates simultaneously.
• Model Architecture Assemblers - Combine pre-built core, interaction, and sequence layers to build bespoke prediction architectures.
• Modular Layer Compositions - Provides pre-built core, interaction, and sequence layers as interchangeable building blocks for custom prediction architectures.
• CTR Predictions - Fit complex models to data and generate predictions through a simple fit and predict interface.
• Model Training - Fit and evaluate complex deep learning models using standard fit and predict methods without writing training loops.
• User Behavior Sequence Training - Process user behavior sequences with attention-based architectures like DIN and DIEN for personalized predictions.
• Variable-Length Sequence Training - Train a deep learning model that accepts variable-length sequence inputs by padding and encoding them as sparse features.
• Binary Classification Training - Train a deep learning model on sparse and dense features to predict a binary outcome like ad click-through rate.
• Click-Through Rate - Train a deep learning model on user behavior data to predict ad or item click probability.
• Click-Through Rate Predictors - Construct deep learning architectures for click-through rate prediction using modular components for feature interactions and user behavior.
• User Behavior - Processes sequences of user actions with attention and recurrent structures to model evolving user preferences.
• Sequence-Aware CTR Libraries - Library for processing user behavior sequences with attention mechanisms to model evolving user interests in CTR prediction.
• Sparse-to-Dense Embedding Tables - Provides configurable embedding tables that transform sparse categorical inputs into dense vectors for CTR prediction.
• Click-Through Rate Predictors - Construct and train deep neural networks for click-through rate prediction using modular components.
• Sequential - Processes user behavior sequences with attention and recurrent structures to model evolving user preferences.
• Squeeze-and-Excitation Attention - Uses squeeze-and-excitation attention to dynamically rank feature importance per instance for CTR prediction.
• Per-Instance Feature Importance - Ranks features by relevance per instance using a squeeze-excitation network to weight feature importance dynamically.
• Adaptive-Order Crosses - Discovers arbitrary-order feature combinations from data using a logarithmic transformation layer.
• Attention-Weighted Feature Pairs - Weights the importance of different feature pairs using a neural attention mechanism to learn feature interactions.
• Interaction Merging Layers - Combine multiple feature interaction patterns through dedicated interaction layers.
• Multi-Head Self-Attention Interactions - Allows each feature to attend to all others across multiple heads to automatically form meaningful higher-order features.
• Post-Click Conversion Estimators - Compute conversion probability across the full impression space using a multi-task sequential prediction pattern.
• Progressive Layer Separation - Route information through progressive layers that split shared and task-specific components for better multi-task learning.
• Feature Relationship Attention Heads - Implements multi-head self-attention to model feature relationships and automatically form meaningful higher-order features.
• Adaptive-Order Feature Cross Layers - Ships a logarithmic transformation layer that discovers arbitrary-order feature crosses from input embeddings.
• Deep and Cross Networks - Combine deep neural networks with explicit cross layers using DCN-M and DCN-Mix architectures.
• Memorization and Generalization Combinations - Join handcrafted feature inputs with learned deep representations to produce a single unified prediction.
• Model Component Assembly - Combines pre-built core, interaction, and sequence layers to construct bespoke prediction architectures without writing training loops.

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