Dynamic Feature Interaction for Multi-Domain CTR Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing personalized recommendation systems face challenges in accurately predicting click-through rates (CTR) across multiple domains due to differing data distributions, leading to suboptimal performance in single-domain models and resource-intensive multi-domain models.
Innovation Solution
A neural network model that considers interaction features between domains by determining domain features based on distribution, encoding them into a dynamic network, and using a feature interaction network for accurate CTR prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-domain model is used for CTR prediction, then the model structure is simple and resource consumption is low, but prediction accuracy deteriorates due to differing data distributions across domains
Solution Approach 1:
The patent segments the feature processing into domain-specific and common parts. Domain-specific feature towers are created for each domain to handle domain-specific data distributions, while a shared bottom layer processes common features. This segmentation allows the model to adapt to different domains without requiring completely separate models, thus maintaining reasonable complexity while improving accuracy.
Solution Approach 2:
The patent introduces dynamic routing mechanisms that allow the model to dynamically select which domain tower to use based on the input domain identifier. This dynamic adaptation enables the model to switch between different domain-specific processing paths, improving prediction accuracy across varying domains while maintaining a unified model structure.
2Measurement precision
If multi-domain models are used for CTR prediction, then prediction accuracy improves by considering domain differences, but resource consumption and model complexity increase
Solution Approach 1:
The patent merges multiple domain-specific feature towers with a shared common feature processing layer. The domain towers process domain-specific features while the shared bottom layer handles common features, and their outputs are combined for final prediction. This merging approach allows the model to capture domain-specific characteristics while sharing computational resources, thus improving accuracy without linearly increasing complexity.
Solution Approach 2:
The patent creates a multi-functional model structure where the shared bottom layer can process features across all domains, while domain-specific towers provide specialized processing when needed. This universal base with specialized extensions allows the model to serve multiple domains efficiently, improving accuracy without requiring separate dedicated models for each domain.
3Measurement precision
If domain features are extracted and processed through dynamic networks, then feature interaction accuracy improves, but computational resource consumption increases
Solution Approach 1:
The patent applies local quality by processing features differently based on their domain-specificity. Common features are processed once in the shared bottom layer, while only domain-specific features require processing through their respective domain towers. This localized specialized processing improves feature interaction accuracy for domain-critical features while avoiding redundant computation for common features.
Solution Approach 2:
The patent implements partial action by selectively applying domain-specific processing only where needed rather than uniformly across all features. The dynamic routing mechanism enables the model to apply complex domain-specific transformations only when domain identifiers indicate the need, thus improving accuracy for domain-critical interactions while reducing overall computational resource consumption.
Data Source
AI summary
A data processing method includes: determining a domain feature in a plurality of features based on distribution of each of the plurality of features of to-be-processed data; obtaining a plurality of corresponding converted features by separately inputting a plurality of eigenvectors corresponding to the plurality of features to a dynamic network, where a dynamic network parameter of the dynamic network is obtained based on the domain feature; and obtaining a network output by inputting the plurality of converted features to a feature interaction network.