Large-Scale Ranking AI With Residual DCN Feature Calibration
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Solution Overview
Problem
Networking platforms face challenges in generating precise recommendations due to difficulties in identifying which features or combinations of features are most important for prediction tasks, leading to inaccurate and inefficient content delivery and advertising.
Innovation Solution
An AI system utilizing a residual deep and cross network (DCN) with attention data structures and an isotonic calibration layer to enhance feature learning and calibration, focusing on important feature interactions and improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used for feature selection and prediction, then the system is simpler to implement, but the prediction accuracy and ability to identify important feature interactions deteriorates
Solution Approach 1:
The model is segmented into distinct functional components: deep network for non-linear feature transformations, cross network for feature interactions, and attention mechanism for prioritizing important features. This segmentation allows each component to specialize in specific aspects of feature processing, improving prediction accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent introduces an attention dimension that operates alongside the traditional feature space. The attention mechanism computes attention scores that weight the importance of different features and feature interactions, adding a new dimension of information processing that enhances prediction accuracy without fundamentally increasing model complexity.
2Measurement precision
If comprehensive feature combinations are analyzed to improve prediction accuracy, then the prediction quality improves, but the computational complexity and processing time increases
Solution Approach 1:
The deep network performs preliminary feature transformations and extractions before the main prediction process. By pre-processing features to capture non-linear relationships and important interactions in advance, the model reduces the computational burden during inference, maintaining high prediction accuracy while decreasing processing time.
Solution Approach 2:
The attention mechanism serves as an intermediary between the deep network and cross network, filtering and prioritizing feature interactions based on their importance. This intermediary selectively processes only the most relevant features, reducing computational complexity and processing time while preserving prediction accuracy.
3Measurement precision
If more features and feature interactions are considered, then the prediction accuracy improves, but the model difficulty in identifying important features increases
Solution Approach 1:
The attention mechanism acts as an intermediary that automatically identifies and weights important features and feature interactions. By computing attention scores, the model highlights which features contribute most to predictions, making feature importance identification straightforward and interpretable while maintaining high prediction accuracy.
Solution Approach 2:
The attention scores can be interpreted as a form of 'coloring' or highlighting different features based on their importance. Features with higher attention scores are effectively 'highlighted' as more important, making it easy to identify and interpret which features drive predictions without increasing model complexity.
Data Source
AI summary
Embodiments are generally directed to artificial intelligence (AI) and machine learning (ML) techniques for networking platforms, such as a social networking system or a connections networking system. Some embodiments are particularly directed to an AI system implementing ML techniques to support automated networking platform services for members of a networking platform, such as serving content, providing job recommendations, performing feed ranking, serving targeted advertising, predicting advertising click-through-rates (CTR), and other types of networking platform services to engage and provide value to members. In one embodiment, for example, the AI system utilizes an industrial large scale ranking model. Other embodiments are described and claimed.