Bayesian Framework for Multi-Version Website Testing
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Solution Overview
Problem
Existing methods for evaluating website changes, such as A-to-B testing and Thompson sampling, are limited in testing multiple versions and factors affecting website traffic, as they typically consider only a maximum of two likelihoods and a limited number of versions.
Innovation Solution
A Bayesian framework that includes prior and posterior distribution models to model and test multiple versions of a website, using user interaction data to determine posterior distributions and select the best version based on these distributions for hosting on servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If A-to-B testing or Thompson sampling is used to evaluate website changes, then the testing process is simple and manageable, but the number of website versions and factors that can be tested is limited to a maximum of two likelihoods
Solution Approach 1:
The patent segments the website evaluation problem by treating each website version and each factor as separate dimensions that can be independently modeled. Instead of limiting testing to two versions, the system divides the problem into multiple version segments (A, B, C, etc.) and multiple factor segments (design, content, technology), allowing comprehensive testing across all combinations without overwhelming complexity.
Solution Approach 2:
The patent adds a new dimension to traditional A-to-B testing by introducing a multi-version framework that evaluates not just version comparisons but also factor interactions simultaneously. This dimensional expansion allows the system to test multiple website versions and factors in parallel, transforming the limitation from a 2-version constraint to an n-version capability while maintaining statistical rigor through Bayesian modeling.
2Measurement precision
If multiple website versions and factors are tested simultaneously, then the comprehensiveness of evaluation improves, but the complexity of the testing approach increases significantly
Solution Approach 1:
The patent creates a universal Bayesian framework that serves multiple functions simultaneously: it evaluates website versions, assesses factor impacts, models user interactions, and identifies optimal configurations all within a single statistical system. This multi-functional approach allows comprehensive evaluation of multiple versions and factors without requiring separate testing methodologies for each aspect, thereby reducing overall system complexity while maintaining measurement precision.
Solution Approach 2:
The patent manages complexity by dynamically adjusting statistical parameters within the Bayesian model based on the number of versions and factors being tested. The system automatically modifies prior distributions, likelihood functions, and posterior calculations to accommodate varying numbers of website versions and factors, allowing comprehensive evaluation while maintaining computational tractability through parameter adaptation rather than structural complexity.
3Ease of manufacture
If traditional Thompson sampling is used, then the computational approach is straightforward, but it only considers a maximum of two likelihoods in the distribution sample
Solution Approach 1:
The patent applies preliminary action by establishing a Bayesian prior distribution that incorporates expectations about multiple website version performances before actual user interaction data is collected. This prior distribution is then updated with observed data to produce posterior distributions for each version, allowing the system to evaluate multiple likelihoods (more than two) in a computationally straightforward manner by extending the basic Thompson sampling update rule to accommodate n versions rather than just 2.
Data Source
AI summary
Methods, systems, and computer-readable storage media for selection of a version of a website from multiple versions of the website, implementations including receiving user interaction data representative of user interactions with respective versions of a website, for each version of the website, determining a posterior distribution, selecting a version of the website based on the posterior distributions, and hosting the version of the website on one or more servers.