Multi-Objective Optimization Model for Content Delivery Performance Replay
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Solution Overview
Problem
Conventional content management systems require lengthy A/B testing cycles to evaluate performance gains from modifying content item delivery parameters, which can be time-consuming and resource-intensive, especially when testing multiple parameter modifications.
Innovation Solution
Implementing a multi-objective optimization model that uses historical user interaction data and content item selection data to simulate potential performance gains from parameter modifications, allowing for faster evaluation and decision-making on whether to apply or further test modified parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If A/B testing is performed to evaluate parameter modifications, then performance improvement can be determined, but testing duration becomes excessively long
Solution Approach 1:
The system performs preliminary filtering of parameter modifications using business logic rules before conducting full A/B testing. This preliminary action identifies modifications likely to yield positive performance gains, allowing the system to skip unnecessary lengthy testing cycles for modifications predicted to be ineffective, thus reducing overall testing duration while maintaining evaluation accuracy for promising candidates
Solution Approach 2:
The system creates a virtual copy of the A/B testing process by simulating parameter modifications and their expected outcomes using historical data and machine learning models. This virtual testing environment allows rapid evaluation of multiple parameter modifications without requiring actual user exposure, significantly reducing testing time while preserving the ability to make accurate performance predictions
2Adaptability or versatility
If multiple parameter modifications are tested sequentially using A/B testing, then comprehensive evaluation is achieved, but testing cycle extends significantly
Solution Approach 1:
The system performs preliminary filtering of parameter modifications using business logic rules before conducting full A/B testing. This preliminary action identifies modifications likely to yield positive performance gains, allowing the system to skip unnecessary lengthy testing cycles for modifications predicted to be ineffective, thus reducing overall testing duration while maintaining evaluation accuracy for promising candidates
Solution Approach 2:
The system dynamically adjusts the testing strategy by prioritizing parameter modifications based on predicted performance gains. Rather than testing all modifications sequentially with equal resources, the system adaptively allocates testing resources to modifications most likely to succeed, enabling comprehensive evaluation of multiple parameters while maintaining high testing efficiency through dynamic resource allocation
Data Source
AI summary
Technologies for determining performance gains for content item delivery based on modifications to content item selection parameters are provided. The disclosed techniques include implementing a multi-objective optimization model for content item selection using a value for a parameter. The model generates a first plurality of scores and a first ranking for content items. Subset of content items is selected for delivery based on the first ranking. New values for the parameter are identified and for each new value, the content item selection event is replayed. A second plurality of scores and a second ranking is generated for the content items, where the second ranking is different from the first ranking. A third plurality of scores and a third ranking is generated where the third ranking matches the second ranking. A set of gains is calculated for each new value, where each gain corresponds to a different objective of the model.