Hierarchical Feature Tree for Dynamic Advertising Model Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing online advertising systems struggle to adapt to dynamic changes in market conditions, such as unpredictable news events and trends, leading to inefficiencies in selecting entities for advertising content delivery due to stale models and features.
Innovation Solution
A system that automatically detects degraded model features by comparing performance metrics across time windows, updates the model with new features from a hierarchical feature tree, and incorporates prospective features to maintain campaign efficiency, enabling real-time adjustments in advertising campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a behavioral model is built using historical features to select entities for advertising, then the model provides stable and reliable entity selection, but the model becomes stale and ineffective when market conditions change dynamically
Solution Approach 1:
The patent implements dynamic model updating by continuously monitoring feature performance metrics and automatically retraining the behavioral model when degradation is detected. This transforms the static historical model into a dynamic system that adapts to changing market conditions while maintaining stability through systematic update triggers based on performance thresholds.
Solution Approach 2:
The system establishes a feedback loop where model performance is continuously measured against conversion data, and degradation detection triggers automatic model retraining. This closed-loop feedback mechanism ensures the model maintains reliability while adapting to market changes by using performance metrics as the feedback signal for when updates are needed.
2Adaptability or versatility
If manual model updates are performed to adapt to market changes, then the model can be customized for specific conditions, but the process requires significant operator time and effort
Solution Approach 1:
The system implements self-service through automated feature degradation detection and model retraining. The behavioral model automatically monitors its own performance, identifies when features degrade, and triggers retraining without human intervention. This eliminates the need for manual model updates while maintaining full adaptability to market changes.
Solution Approach 2:
The system automatically changes model parameters by retraining the behavioral model with updated conversion data when degradation is detected. This automated parameter adjustment replaces manual customization efforts, allowing the model to adapt to market conditions without requiring operator time or expertise in model tuning.
3Measurement precision
If comprehensive feature analysis is performed to detect degraded features, then accurate model updates can be made, but the computational complexity and processing time increase
Solution Approach 1:
The system applies local quality by focusing computational resources only on features that show degradation signs. Instead of analyzing all features comprehensively, the system identifies and investigates only those features whose performance metrics indicate degradation, thereby reducing overall computational complexity while maintaining measurement precision for critical features.
Solution Approach 2:
The system performs partial analysis by conducting comprehensive feature performance measurement only when degradation is detected through initial monitoring. This two-stage approach uses minimal initial monitoring followed by detailed analysis only when necessary, reducing average computational complexity while maintaining accuracy when updates are actually needed.
Data Source
AI summary
Automatic performance triggered campaign adjustment. A hierarchical feature tree is generated. Each child node's feature is more specific than its respective parent node's feature. The discovery system creates a behavioral model comprising features of the feature tree which is used in the operation of an advertising campaign. A degraded model feature is detected at the discovery system by comparing a performance metric of a model feature from two different time windows. The discovery system matches a node of the feature tree with the degraded feature and selects a prospective model feature from an ancestor node of the matching feature's node. An estimated performance metric for the prospective model feature is determined and the results are used to decide if the prospective model feature should be incorporated into an updated model or not. The model can be updated with a new model feature selected from one or more prospective model features.