Content Group Ranking by Request Coverage Gain
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Solution Overview
Problem
Existing campaign management platforms face inefficiencies due to overpopulation of content groups, leading to reduced accuracy in content delivery and performance tracking, as well as increased computational and storage requirements, when ranking is based solely on request coverage without considering relative gains between groups.
Innovation Solution
Ranking candidate content groups based on request coverage gain, along with optional criteria such as content strength, theme importance, and theme to resource identifier relevance, to select unique groups that maximize overall request coverage while avoiding redundancy and adhering to technical capacity limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content groups are ranked solely by request coverage, then more content requests are captured, but redundant overlapping content groups increase computational and storage requirements
Solution Approach 1:
The patent changes the ranking parameter from absolute request coverage to request coverage gain (relative improvement). This parameter transformation allows the system to identify content groups that provide incremental value, eliminating redundant groups while maintaining comprehensive coverage. The system calculates request coverage gain as the difference between a content group's request coverage and the average request coverage of existing groups, enabling selective inclusion of only high-value groups.
2Measurement precision
If more content groups are added to capture all content requests, then content delivery accuracy improves, but the system exceeds technical capacity limits
Solution Approach 1:
The system transforms the selection criterion from absolute metrics (request coverage, content strength) to a composite scoring mechanism that ranks content groups by their relative contribution. By calculating a composite score that incorporates request coverage gain, content strength, theme importance, and theme-resource identifier relevance, the system can prioritize high-value groups and stop adding groups once technical capacity limits are approached, maintaining accuracy within available resources.
Solution Approach 2:
The system uses technical capacity limits as a feedback signal to control content group addition. When capacity thresholds are approached, the system stops adding new content groups, using the composite score ranking to ensure only the most valuable groups are included. This feedback mechanism prevents system overload while maintaining optimal content delivery accuracy within resource constraints.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable storage media for content group generation for a content delivery campaign. Content groups are generated from a resource identifier and a description. Digital content items are created for each content group, including digital content from the resource identifier and the description, as well as new digital content items. Candidate content groups are ranked according to request coverage gain and optionally one or more other ranking criteria. Request coverage gain is a measure of how much more request coverage is gained through keywords of one content group relative to the request coverage of one or more other content groups. By ranking according to request coverage gain, the selected candidate content groups are differentiated relative to one another, capturing potential content requests that would otherwise be missed by a campaign of content groups not selected based on request coverage gain.