Information-Greedy Multi-Arm Bandits for UI Testing
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Solution Overview
Problem
Current website experience testing methods, such as A/B testing and traditional multi-arm bandit approaches, fail to maximize both test power and in-test rewards, with A/B tests being inefficient and MAB approaches having low testing power due to unbalanced sample sizes.
Innovation Solution
The implementation of information-greedy and info-reward-greedy multi-arm bandit algorithms, which calculate ratios of experience cumulative rewards and user traffic splits to determine the most effective website experience to serve, optimizing test power and rewards while maintaining or exceeding the performance of A/B tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If A/B testing is used to test website experiences, then test power is maintained with equal sample sizes, but in-test rewards are not maximized due to random assignment
Solution Approach 1:
The patent implements dynamic traffic allocation that adjusts the proportion of users assigned to each experience version based on real-time performance feedback. Unlike static A/B testing with fixed 50/50 splits, the system continuously modifies assignment probabilities to balance exploration and exploitation, thereby maintaining test power while maximizing cumulative rewards during the testing period.
Solution Approach 2:
The system incorporates continuous feedback loops where performance metrics from each experience version are monitored and fed back into the allocation algorithm. This feedback mechanism enables the system to learn from observed outcomes and adjust traffic distribution dynamically, resolving the contradiction between maintaining statistical power and optimizing in-test rewards.
2Productivity
If traditional multi-arm bandit approaches are used to maximize in-test rewards, then rewards are improved, but test power decreases due to unbalanced sample sizes
Solution Approach 1:
The patent modifies the traditional multi-arm bandit algorithm by introducing constraints and adjustments to the allocation parameters. Specifically, it implements modified allocation rules that prevent extreme imbalances in sample sizes across experience versions, ensuring that each version receives sufficient exposures for statistically valid comparisons while still allowing the system to optimize for rewards.
Solution Approach 2:
The system employs asymmetric allocation strategies where the degree of traffic skew toward better-performing versions is controlled and balanced. Rather than allowing complete asymmetry that would concentrate all traffic on the best version, the system maintains controlled asymmetry that optimizes rewards while preserving minimum sample sizes for reliable statistical inference.
3Reliability
If more users are exposed to each experience version to maintain test power, then test power is improved, but the testing period is extended
Solution Approach 1:
The system performs preliminary actions by pre-calculating allocation strategies and confidence thresholds before the testing period begins. This allows the system to determine in advance the minimum sample sizes required for statistical power and structure the dynamic allocation to reach these thresholds efficiently, reducing the overall testing period while maintaining power.
Solution Approach 2:
The patent implements mechanisms to skip unnecessary testing phases by using sequential analysis and early stopping rules. When sufficient statistical power is achieved or when one version clearly outperforms others beyond a predetermined threshold, the system can terminate the test early, rushing through the testing period without compromising the reliability of the results.
Data Source
AI summary
A method for determining a user experience for an electronic user interface includes defining a test period for testing two or more versions of an electronic user interface, receiving, from each of a plurality of users during the test period, a respective request for the electronic user interface, determining, for each of the plurality of users, a respective version of the two or more versions of the electronic user interface by maximizing test power during the test period while maintaining higher in-test rewards than an A/B test or maximizing the rewards during the test period while maintaining a test power no worse than an A/B test, and causing, for each of the plurality of users, the determined version of the electronic user interface to be delivered to the user.