Dynamic Frequency Capping for Conflicting Delivery Signals
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Solution Overview
Problem
Conventional content delivery systems face challenges in dynamically adjusting frequency capping rules to accommodate varying user preferences and content provider objectives, leading to inefficiencies and potential 'brain split' situations due to conflicting signals.
Innovation Solution
Implement a central control component that utilizes machine learning to generate dynamic frequency capping rules based on multiple signals, including forecasting curves, content provider settings, and user data, ensuring seamless content delivery aligned with objectives and user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If frequency capping rules are made static, then system simplicity is maintained, but adaptability to user preferences and content provider objectives deteriorates
Solution Approach 1:
The patent implements dynamic frequency capping rules that automatically adjust based on user behavior patterns and content performance metrics. The system transitions from static to dynamic Fcap rules, allowing real-time adaptation to changing user preferences and content provider objectives without manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor user engagement metrics, content performance, and delivery effectiveness. This feedback loop enables the frequency capping rules to be continuously optimized based on actual performance data, ensuring adaptability while maintaining manageable system complexity through automated adjustments.
2Productivity
If multiple signals are used to control content delivery, then delivery optimization is improved, but system complexity and conflict resolution difficulty increase
Solution Approach 1:
The patent merges multiple control signals into a unified frequency capping framework. By consolidating diverse delivery parameters and objectives into a single Fcap rule system, the patent reduces the complexity of managing multiple conflicting signals while maintaining the optimization benefits of multi-signal control.
Solution Approach 2:
The system introduces an intermediary layer that processes and reconciles multiple control signals before applying frequency capping rules. This intermediary mechanism resolves conflicts between different delivery objectives and signals, simplifying the overall control architecture while preserving delivery optimization capabilities.
3Reliability
If frequency capping is increased to prevent repetitive content, then user engagement quality is improved, but content delivery speed deteriorates
Solution Approach 1:
The patent implements dynamic frequency capping that adjusts the cap level based on real-time user engagement metrics and content performance. When users show high engagement with content, the Fcap rule increases delivery frequency to maintain engagement quality. When engagement decreases, the cap reduces frequency to prevent repetition, thus balancing reliability and speed dynamically.
Solution Approach 2:
The system changes the frequency capping parameter dynamically based on observed user behavior patterns and content effectiveness. By adjusting the Fcap value as a variable parameter rather than a fixed constraint, the system optimizes the balance between preventing repetitive content and maintaining adequate delivery speed to meet user needs.
Data Source
AI summary
Systems and methods are directed to selecting and delivering targeted content. In response to a request from a user, a central control component accesses content delivery data and content provider settings. Based on the content delivery data and the content provider settings, the central control component generates content delivery settings that include a plurality of frequency capping (Fcap) rules. The content delivery settings are transmitted to a serving system. A determination component of the serving system accesses user data associated with the user that indicates user preferences. Based on the content delivery settings and the user data, the determination component selects a piece of content to deliver to the user. A delivery component then delivers the piece of content to the user.