Dynamic Component Selection for Displayable Files
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Solution Overview
Problem
Existing solutions for the multi-armed bandit problem, such as the Biased Robin method, are not well-suited for component selection in displayable files due to factors like non-independence of components, variable availability, and changing component values, which differ from traditional multi-armed bandit scenarios.
Innovation Solution
A component management server selects components for displayable files by determining an initial ordering based on estimated values and standard errors, considering priority and availability, and dynamically updates confidence data to optimize component selection across multiple portions of a displayable file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional multi-armed bandit solutions like Biased Robin are used for component selection, then the selection process follows a standardized approach, but the solutions fail to account for non-independence of components, variable availability, and changing component values
Solution Approach 1:
The patent implements dynamic component selection by continuously updating confidence data and re-ordering components based on changing conditions. The system adapts to variable component availability and changing values by recalculating scores and re-selecting components in real-time, rather than using static selection methods.
Solution Approach 2:
The patent segments the component selection process into distinct portions of displayable files, allowing different selection strategies to be applied to different portions. This enables the system to handle non-independence of components by treating each portion as a separate decision unit while maintaining overall coordination.
2Measurement precision
If components are selected based on estimated values alone, then the selection process is simple, but the accuracy of component effectiveness prediction is insufficient
Solution Approach 1:
The patent implements feedback mechanisms by tracking actual user actions taken on displayed components and using this information to update confidence data. The system measures the effectiveness of selected components and feeds this information back into the selection process to improve future selections, thereby increasing measurement precision.
Solution Approach 2:
The patent replaces simple value-based selection with a more sophisticated confidence-based selection mechanism that incorporates statistical measures (standard errors) and probabilistic scoring. This substitution of the selection mechanism improves precision by accounting for uncertainty in component effectiveness estimates.
3Loss of information
If the system explores new components frequently, then more information about component values is gathered, but the total reward from proven effective components decreases
Solution Approach 1:
The patent dynamically changes the selection parameters by adjusting the balance between exploration and exploitation based on confidence levels. When confidence in component values is high, the system exploits known effective components; when confidence is low, it explores new components. This parameter adjustment optimizes the trade-off between information gathering and reward maximization.
Solution Approach 2:
The patent implements dynamic adjustment of the exploration-exploitation balance by continuously updating confidence data and adapting the selection strategy. The system transitions from exploration to exploitation as confidence increases, and vice versa, optimizing the trade-off between gathering information and maximizing rewards from proven components.
4Productivity
If components are selected without considering priority and availability, then the selection process is faster, but the effectiveness of content presentation is reduced
Solution Approach 1:
The patent performs preliminary actions by pre-calculating component scores, ordering components by confidence, and identifying available components before the actual selection moment. This preliminary preparation allows the system to make fast selections while still considering priority and availability, as the heavy computational work is done in advance.
Solution Approach 2:
The patent applies different selection criteria to different portions of displayable files based on their priority and context. High-priority portions receive more careful selection with full consideration of availability and confidence, while lower-priority portions use faster selection methods. This local differentiation maintains effectiveness while optimizing speed.
Data Source
AI summary
Systems and methods are provided for selecting components to include in portions of a displayable file. Selecting the components may include determining an order of the components for each portion of the displayable file. The components' order for a given portion may be based on a score for each component, where a component's score is based on an estimated value and standard error associated with the component. The component to include in each portion of the displayable file may be selected based at least in part on the determined component order for each portion and a predetermined priority of each portion.