Linear Programming Dynamic Blending Model for Content Placement
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Solution Overview
Problem
Existing methods for displaying sponsored and organic content in online networks, such as social networking services, face inefficiencies due to fixed slotting approaches that do not adapt to user preferences and content-specific engagement rates, leading to suboptimal user interaction and revenue optimization.
Innovation Solution
A linear programming-based dynamic blending model that optimizes the placement of sponsored and organic content by maximizing view proportion while maintaining constraints, using offline calculations to determine runtime parameters and incorporating position bias to enhance engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed slotting is used to display sponsored and organic content, then the system structure is simple and easy to implement, but the user engagement and revenue optimization are suboptimal
Solution Approach 1:
The patent implements dynamic slotting where the allocation of display slots between sponsored and organic content is not fixed but dynamically adjusted based on real-time factors including user preferences, content characteristics, and engagement metrics. The system continuously optimizes the blending ratio to maximize revenue while maintaining user engagement, replacing the static fixed slotting approach with an adaptive dynamic model that responds to changing conditions.
2Device complexity
If fixed slotting is used to display sponsored and organic content, then the system complexity is low, but the adaptability to user preferences and content-specific engagement rates is poor
Solution Approach 1:
The patent changes the parameters of the display system by introducing dynamic variables for slot allocation that are adjusted based on user preferences, content engagement rates, and contextual factors. Instead of fixed parameters, the system uses probabilistic models and optimization algorithms to determine the optimal blending ratio, allowing the display configuration to adapt to different users, content types, and situational contexts while managing complexity through modular architecture.
3Productivity
If more slots are devoted to organic content, then user engagement with organic content increases, but the revenue generation from sponsored content decreases
Solution Approach 1:
The patent dynamically adjusts the proportion of sponsored versus organic content in display slots based on optimization objectives. The system can shift the blending ratio to prioritize either engagement metrics or revenue generation depending on the current context, user behavior, and content characteristics, allowing flexible trade-off management between these two competing goals through parameter optimization rather than fixed allocation.
Solution Approach 2:
The system implements dynamic balancing between sponsored and organic content placement, where the allocation is continuously optimized based on real-time feedback from user interactions and engagement metrics. This dynamic approach allows the system to adaptively find the optimal balance point between maximizing user engagement and generating revenue from sponsored content, rather than using a static fixed ratio.
Data Source
AI summary
In an example embodiment, a blending model is presented based on a linear programming approach. The blending model produces a slate of sponsored and non-sponsored pieces of content for display in a graphical user interface, with the ordering and placement of the sponsored and non-sponsored pieces of content selected in order to maximize an objective function. Such an approach can fine tune each piece of content using content-level parameters and holistically examine global constraints and opportunities. It establishes a robust optimization framework that can adapt to content and domain changes without requiring tuning through online experiments.