Content Selection Model Optimizing Combinations via Bandit Strategies
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Solution Overview
Problem
Conventional methods for determining optimal content combinations for user engagement are resource-intensive, require substantial traffic, and are not adaptable to changing conditions, leading to suboptimal results and inefficiencies in content presentation.
Innovation Solution
The use of a content selection model that defines weights for combinations of content items, leveraging bandit strategies and sampling techniques to quickly identify optimal content combinations, reducing the need for full factorial analyses and minimizing network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fully factorial multivariate analysis is used to identify optimal content combinations, then the optimal combination can be identified, but substantial network traffic and computing resources are required
Solution Approach 1:
The patent segments the content presentation into multiple components (e.g., header, body, footer) and tests combinations of these components independently rather than testing all possible full-page combinations. This segmentation reduces the total number of combinations that need to be tested, thereby reducing network traffic while still identifying optimal content combinations through component-level optimization.
Solution Approach 2:
The patent applies partial action by testing only the most promising content combinations based on initial analysis rather than exhaustively testing all possible combinations. It uses heuristics and machine learning to identify and test a subset of combinations that are most likely to yield optimal results, reducing network traffic while maintaining effectiveness.
2Measurement precision
If fully factorial multivariate analysis is used to identify optimal content combinations, then the optimal combination can be identified, but months of traffic testing are required
Solution Approach 1:
The patent performs preliminary analysis and pre-testing of content components before conducting full combination testing. It uses machine learning models to predict which combinations are most likely to succeed, allowing the system to focus testing efforts on promising combinations from the outset rather than testing all combinations equally, thereby reducing the time required to identify optimal combinations.
Solution Approach 2:
The patent implements continuous optimization where the system learns from each test result and dynamically adjusts which combinations to test next. This continuous feedback loop allows the system to converge on optimal combinations more quickly by continuously refining its understanding of what works, rather than using static, pre-planned testing sequences.
3Measurement precision
If traditional multivariate analysis is used, then content combinations can be tested, but the results are not adaptable to changing conditions and user preferences
Solution Approach 1:
The patent implements dynamic content optimization where the system continuously adapts to changing user preferences and conditions. It uses machine learning models that are trained on user interaction data and updated in real-time, allowing the system to dynamically adjust content combinations based on current user behavior, device type, location, and other contextual factors, making the optimization results adaptable rather than static.
Solution Approach 2:
The patent incorporates feedback mechanisms where user interactions with content combinations are continuously monitored and fed back into the optimization system. This feedback loop allows the system to learn from actual user behavior and adjust future content selections accordingly, ensuring adaptability to changing conditions and preferences rather than relying on predetermined static combinations.
Data Source
AI summary
An optimal combination of content items may be determined and served to increase the likelihood of a predefined user interaction. A user can request content which may include an entity (such as a web page, document, advertisement, and the like) that has multiple components through which content items may be provided. For example, a web page may include multiple slots where content items may be displayed on the web page. Each component may be associated with multiple possible content items, resulting in many combinations of layouts for an entity. A content server may determine which layout to provide using a content selection model that is weighted based on a likelihood of groupings of content items resulting in a user interaction that satisfies a success condition (e.g., selecting a hyperlink, selecting a content item, initiating a transaction, etc.).