Backend Content Curation Using Marginal Value and Similarity
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Solution Overview
Problem
Conventional methods of digital content curation result in bloated collections with numerous poor-performing and similar content items, leading to excessive storage requirements and degraded performance in digital advertising campaigns.
Innovation Solution
A system that monitors performance metrics and similarity metrics for digital content items within a collection, determining a marginal value to assess their contribution to the overall collection, and either removes or maintains items based on this value, using generative AI techniques for automatic curation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional methods of content curation are used to maintain large content collections, then the quantity of content items is increased, but storage requirements become excessive and performance degrades
Solution Approach 1:
The system automatically identifies and removes low-value content items from collections based on performance metrics and similarity analysis. By discarding redundant or poor-performing content, the system maintains optimal collection size and performance while reducing storage and computational resource consumption.
Solution Approach 2:
The system extracts and removes specific low-value content items from collections based on automated analysis of performance data and similarity metrics. This selective extraction eliminates unnecessary content while preserving high-value items, resolving the contradiction between maintaining quantity and reducing resource usage.
2Quantity of substance
If conventional methods of content curation are used, then content collections grow in size, but the quality and diversity of content deteriorate due to bloating with similar poor-performing items
Solution Approach 1:
The system continuously monitors content performance and automatically discards low-quality, redundant content items while recovering and maintaining high-performing diverse content. This dynamic discarding process ensures collection quality is maintained regardless of size.
Solution Approach 2:
The system changes the evaluation parameters from simple quantity-based metrics to composite metrics that include performance data and similarity analysis. By changing how content value is measured, the system can maintain quality and diversity even as collection size varies.
3Reliability
If manual content curation is performed to maintain quality, then content quality is preserved, but the complexity and resource requirements of management increase
Solution Approach 1:
The system performs automated content evaluation and curation without manual intervention. By enabling the system to self-manage content quality through automated performance tracking and similarity analysis, manual management complexity is eliminated while maintaining high content quality.
Solution Approach 2:
The system implements continuous feedback loops that automatically monitor content performance and trigger appropriate curation actions. This automated feedback mechanism maintains content quality without requiring manual oversight, reducing management complexity.
4Quantity of substance
If all content items are retained in collections regardless of performance, then no content is lost, but storage space and processing power are wasted on poor-performing items
Solution Approach 1:
The system selectively discards poor-performing content items while recovering and preserving high-value content. This intelligent discarding process eliminates waste of storage and computational resources on low-value items while maintaining retention of valuable content.
Solution Approach 2:
The system changes from a binary retain/all-keep approach to a nuanced parameter-based evaluation system that considers performance metrics and similarity. This parameter change enables selective retention that minimizes resource waste while maintaining content value.
Data Source
AI summary
A method of curating content includes determining a marginal value of a digital content item to a digital content collection. The method also includes monitoring one or more performance metrics for a digital content item based on user interactions with the digital content item; determining one or more similarity metrics based on a vector embedding of the digital content item and one or more other vector embeddings of one or more other digital content items in the digital content collection; determining the marginal value of the digital content item to the digital content collection based on the one or more performance metrics of the digital content item and at least one similarity metric of the one or more similarity metrics; and based on the marginal value, either removing the digital content item from the digital content collection, or maintaining the digital content item in the digital content collection.