Dynamic Pacing Parameter Adjustment for Content Delivery
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Solution Overview
Problem
In a guaranteed delivery environment, it is challenging to efficiently allocate supplemental content slots to meet pacing constraints and optimize personalization objectives, especially in dynamic systems with thousands of competing content deliveries, where prior methods struggle to converge to desired pacing curves and achieve optimal user experience or conversion actions.
Innovation Solution
The system adjusts parameters to meet an expected pacing behavior for each line item, converging to a desired pacing curve by dynamically updating parameters based on actual delivery performance, using a pacing behavior model that ensures efficient and optimal delivery of supplemental content, balancing pacing constraints and personalization objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic adjustment of delivery strategy is used to satisfy pacing constraints, then pacing constraints are met, but convergence time is long and personalization objective is not optimized
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal parameter adjustments based on current delivery status and pacing requirements. Instead of waiting for periodic feedback, the system proactively adjusts parameters in advance to guide the delivery toward the pacing curve, reducing convergence time while maintaining constraint satisfaction.
Solution Approach 2:
The system implements continuous feedback mechanisms that monitor actual delivery performance against the pacing curve in real-time. This feedback is used to dynamically adjust delivery parameters, enabling the system to respond to deviations immediately rather than waiting for periodic updates, thus accelerating convergence while maintaining pacing constraint satisfaction.
2Reliability
If independent feedback control is used for each line item, then pacing constraints are addressed, but system complexity increases and optimal personalization cannot be achieved
Solution Approach 1:
The system merges the control of multiple line items into a unified delivery optimization framework. By combining parameter adjustments across all line items and considering their interactions, the system reduces overall complexity while still addressing individual pacing constraints. This unified approach enables simultaneous optimization of personalization objectives across the entire content delivery system.
3Productivity
If black box methods like neural networks are used to adjust all line items, then delivery optimization is attempted, but convergence time is unpredictable and personalization objective is not optimized
Solution Approach 1:
The system employs systematic parameter changes based on mathematical models that describe the relationship between delivery parameters and pacing curve convergence. By using analytically derived parameter adjustment rules rather than black-box methods, the system achieves predictable convergence times while maintaining delivery optimization and personalization objective achievement.
Data Source
AI summary
In some embodiments, a method receives information for a delivery of instances of supplemental content for a plurality of line items. A line item is associated with an instance of supplemental content that can be delivered and a pacing curve that describes a pace of delivery over time. The method updates a parameter for the line item to generate an updated parameter based on the delivery of the instances of supplemental content and a desired pacing behavior. The updated parameter is provided to a selection system that uses the updated parameter to select an instance for delivery. The delivery of instances of supplemental content for the line item is adjusted to meet the pacing curve based on a characteristic of the pacing behavior.