CTR Model Architecture Search for Embedding and Feature Interaction
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Solution Overview
Problem
Existing click-through rate prediction models require manual design and expert intervention, leading to inefficiencies, high resource costs, and suboptimal results due to lack of integrated search frameworks for multiple modules, resulting in inconsistent expressiveness and adaptability issues.
Innovation Solution
An integrated architecture searching system that includes search spaces for embedding vector dimensions, feature interaction, and deep network modules, with a unified representation and high-order implicit feature interactions, utilizing a super network training process for joint optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual architecture design is used for each recommendation task, then model performance can be optimized through expert experience, but labor cost and resource consumption for trial and error increase significantly
Solution Approach 1:
The system enables automatic architecture search that performs model design and optimization without manual expert intervention. The architecture search algorithm automatically searches through the search space to find optimal model architectures for different recommendation tasks, eliminating the need for manual trial and error while maintaining high model performance.
Solution Approach 2:
The system changes the approach from manual parameter tuning to automated parameter search. By defining a comprehensive search space that includes embedding dimensions, interaction types, and network depths, the system allows the architecture search algorithm to automatically optimize these parameters, reducing labor cost while improving model performance.
2Ease of operation
If conventional architecture search algorithms are used, then single module search is simplified, but the entire model architecture cannot be searched comprehensively across multiple modules
Solution Approach 1:
The system merges multiple separate search spaces (embedding search space, interaction search space, network search space) into a unified integrated architecture search framework. This allows the system to search for optimal architectures across all modules simultaneously, achieving comprehensive multi-module optimization while maintaining the modularity that makes each component searchable.
Solution Approach 2:
The integrated architecture search system provides a universal framework that can search for optimal architectures across different recommendation tasks and model types. The system handles multiple modules (embedding, interaction, network) within a single unified search process, making it adaptable to various scenarios while maintaining operational simplicity.
3Reliability
If manual expert experience is used for architecture design, then model expressiveness can be optimized, but the design process becomes labor-intensive and inconsistent across different tasks
Solution Approach 1:
The system replaces manual expert design with automated architecture search that consistently optimizes model expressiveness across different tasks. The algorithm automatically learns optimal architectures through systematic search, eliminating the inconsistency inherent in manual design while reducing design process complexity through automation.
Solution Approach 2:
The system uses performance feedback from validation data to guide the architecture search process. By evaluating different architecture configurations and selecting those that perform best, the system automatically optimizes model expressiveness without requiring manual expert intervention, thereby reducing design complexity while maintaining high performance.
Data Source
AI summary
An integrated architecture searching system for a click-through rate prediction model is provided. The system includes a first search space configured to search for embedding vector dimensions of features and determine a matching embedding vector dimension for each pair of the features; a second search space configured to obtain a feature interaction result by searching for a feature interaction sub-network and a feature interaction combination; and a third search space configured to obtain a click-through rate prediction value by incorporating the feature interaction result into an high-order implicit feature interaction search space and performing high-order implicit feature interaction on deep networks of different layers.