Online Content Allocation with Timing Penalty Scoring
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Solution Overview
Problem
Existing online content delivery systems face challenges in efficiently allocating content items, such as advertisements, over a period while maximizing value and meeting allocation commitments, often resulting in unpredictable impression numbers and trade-offs between delivery goals and cumulative value.
Innovation Solution
A method and system that determine a score for each content item based on its weight and timing penalty, calculated from previous impressions, to select and allocate content items effectively, ensuring smooth allocation over time and maximizing average value, by partitioning the delivery period into intervals with soft timing constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content items are allocated to meet commitment requirements over a delivery period, then allocation reliability is improved, but delivery smoothness and predictability deteriorate due to unpredictable impression numbers
Solution Approach 1:
The system performs preliminary actions by calculating timing penalties based on historical allocation data and delivery patterns before actual content allocation occurs. This allows the system to predict and smooth out delivery variations in advance, ensuring both commitment fulfillment and smooth delivery without real-time fluctuations.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual delivery performance against target delivery patterns and adjusting timing penalties accordingly. This feedback loop enables the system to learn from past allocations and improve future delivery smoothness while maintaining reliability in meeting commitment requirements.
2Productivity
If content items are allocated to maximize cumulative value, then advertiser return on investment is improved, but delivery goal achievement deteriorates due to trade-offs between value optimization and commitment fulfillment
Solution Approach 1:
The system changes parameters by dynamically adjusting timing penalties based on multiple factors including delivery stage, historical performance, and value optimization targets. This parameter adjustment allows the system to balance cumulative value maximization with delivery goal achievement, preventing extreme value optimization that would compromise commitment fulfillment.
Solution Approach 2:
The system applies dynamics by making timing penalties adaptive rather than static. The penalties evolve based on real-time delivery progress and value considerations, allowing the system to flexibly balance between maximizing cumulative value and ensuring delivery goals are met, rather than being constrained by fixed allocation rules.
3Productivity
If timing penalties are calculated based on multiple factors including previous impression weights, then allocation optimization is improved, but computational complexity deteriorates
Solution Approach 1:
The system segments the complex timing penalty calculation into distinct components: base timing penalty, delivery stage adjustments, and historical performance factors. This segmentation allows each component to be calculated and adjusted independently, reducing overall computational complexity while maintaining comprehensive optimization based on multiple factors including previous impression weights.
Data Source
AI summary
This specification describes technologies relating to displaying online content. In general, one aspect of the subject matter described in this specification can be embodied in methods that include determining a timing penalty for a content item, the timing penalty based in part on weights of previous impressions the content item has been allocated during a plurality of intervals within a delivery period. The methods may further include determining a weight associated with a pairing of the content item and an impression, the weight based in part on characteristics of the impression. The methods may further include determining a score for the content item, based in part on the weight and the timing penalty. The methods may further include selecting one of a set of matching content items, based in part on the score for the content item, and allocating the selected content item in response to a request.