Digital Component Ranking via Eligibility Scores
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Solution Overview
Problem
Existing decision systems in online networks face challenges in quickly selecting and distributing digital components with electronic documents, leading to potential page load errors and user experience issues due to delayed component delivery.
Innovation Solution
A method that involves receiving a component request, determining a ranking score for digital components based on current and historical eligibility values, and selecting the highest ranked components for distribution, ensuring timely delivery and optimal user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional decision systems are used to select digital components, then system simplicity is maintained, but component delivery is delayed causing page load errors and poor user experience
Solution Approach 1:
The system pre-calculates and stores historical eligibility values for digital components before they are needed. When a component request arrives, the system retrieves pre-computed historical data and current eligibility values, then quickly determines ranking scores without performing complex real-time analysis. This preliminary preparation of data enables fast component selection while maintaining system responsiveness.
2Measurement precision
If component selection is delayed to ensure accurate ranking, then component selection accuracy is improved, but page load errors occur due to timing issues
Solution Approach 1:
The system creates and uses copies of historical eligibility value data that can be quickly retrieved and processed. Instead of performing complex real-time eligibility assessments, the system works with pre-computed copies of eligibility data, enabling fast ranking score calculations that maintain accuracy while meeting tight timing requirements for component delivery.
3Productivity
If more digital components are selected for distribution, then component availability increases, but the likelihood of page load errors increases due to network overhead
Solution Approach 1:
The system dynamically adjusts the number of digital components selected for distribution based on ranking scores derived from eligibility values. By changing the selection parameter from a fixed quantity to a score-based threshold, the system optimizes the balance between component availability and network overhead, ensuring that only the most relevant components are distributed to maintain page load success rates.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing decision systems. In some implementations, methods can include receiving a component request specifying a maximum number of digital components that are capable of being presented on a particular electronic document being rendered at a client device. A ranking score for the given digital component is determined based on a maximum interaction increase factor of formatting available to be applied to the given digital component, the current eligibility value, and the historical eligibility value data. The given digital component is ranked among other available digital components based on the determined ranking score. The given digital component is selected for distribution based on the ranking score of the given digital component being included in the maximum number of highest ranked digital components. The given digital component is transmitted to a client device.