Importance-Aware Machine Learning Model for Edge Ranking
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Solution Overview
Problem
Existing recommender systems face challenges in recommending diverse entities across different node and edge types in online networks due to scale issues and biased training data distributions, leading to difficulties in comparing importance across different edge types and optimizing for different metrics.
Innovation Solution
A single calibrated importance-aware machine learned model is employed, utilizing log-linear regression to calibrate scores across Edge-FPR models and incorporating per-edge type multiplicative factors learned through counterfactual experimentation to optimize for a common downstream metric, ensuring comparable rankings across all combinations of node and edge types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate machine learned models are used for different edge types, then each model can be optimized for its specific metric, but it becomes difficult to compare importance across different edge types due to scale issues
Solution Approach 1:
The patent merges multiple separate machine learned models into a single unified model that processes all edge types together. This unified model uses a common scoring framework that allows direct comparison of importance across different edge types while maintaining the ability to optimize for specific metrics through weighted contributions from different edge type scores.
Solution Approach 2:
The patent transforms the scoring parameters by applying log-linear calibration to normalize scores from different edge types onto a common scale. This parameter transformation enables comparability across edge types while preserving the metric-specific optimization capabilities through calibrated weight adjustments.
2Adaptability or versatility
If training data is collected from multiple edge types, then diverse entity recommendations can be made, but biased training data distributions lead to difficulties in comparing importance across edge types
Solution Approach 1:
The patent applies local quality adjustment by introducing edge-type-specific calibration parameters that adjust the scoring distribution for each edge type individually. This allows the system to handle biased training data distributions by applying localized corrections to each edge type's score distribution while maintaining overall system versatility.
Solution Approach 2:
The patent creates equipotential scoring by calibrating all edge type scores to a common reference scale using log-linear regression. This eliminates the bias introduced by different training data distributions and enables fair comparison of importance across all edge types while preserving recommendation diversity.
3Measurement precision
If a single model is used for all edge types, then comparability across edge types is improved, but the model complexity increases
Solution Approach 1:
The patent segments the single model into modular components: edge-type-specific feature extractors, calibration modules for each edge type, and a unified scoring aggregator. This segmentation maintains comparability across edge types while reducing overall model complexity through structured modularity and reusable components.
Data Source
AI summary
In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.