Prediction Model for A/B Testing Sensitivity
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Solution Overview
Problem
Current controlled experiments in online services, such as A/B testing, face limitations in sensitivity due to restricted user populations and prolonged experiment durations, which hinder efficient data collection and timely decision-making.
Innovation Solution
A method and system that utilize prediction models, like gradient boosting decision trees and linear regression, to calculate predicted user behavior metrics for both control and treatment variants, allowing for the determination of statistically significant differences within a shorter timeframe by combining actual and predicted values, thus enhancing experiment sensitivity without requiring extensive data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the population of users participating in the experiment is increased, then the sensitivity of the experiment is improved, but the feasibility is reduced due to limited web service traffic
Solution Approach 1:
The patent applies preliminary action by using prediction models to forecast future user behavior metrics before the experiment actually occurs. This allows the system to simulate what would happen with larger user populations without actually needing to recruit more users, thereby improving experimental sensitivity while working within traffic constraints.
Solution Approach 2:
The patent uses copying by creating synthetic data through prediction models that replicate what user behavior data would look like if more users participated. Instead of recruiting additional real users, the system generates copies of user behavior patterns based on historical data and prediction algorithms, effectively simulating a larger user population.
2Measurement precision
If the duration of the experiment is extended, then the sensitivity of the experiment is improved, but the productivity is reduced due to fewer experiments that can be conducted within a given period
Solution Approach 1:
The patent applies preliminary action by predicting future user behavior metrics in advance using machine learning models. This allows the experiment to achieve the statistical power of long-duration experiments without actually running them for extended periods, thereby maintaining sensitivity while significantly reducing the actual experiment duration and enabling more experiments per period.
Solution Approach 2:
The patent replaces the mechanical system of actually waiting for extended experiment durations with a computational system that uses prediction models to calculate projected outcomes. Instead of mechanically collecting data over months, the system uses algorithms to compute what the results would be, substituting computational processing for time-based data accumulation.
3Measurement precision
If the amount of observed statistical data is increased, then the sensitivity of the experiment is improved, but the loss of time is increased due to longer data collection periods
Solution Approach 1:
The patent uses copying by generating synthetic statistical data through prediction models that replicate the characteristics of large-volume observed data. Instead of actually collecting vast amounts of user behavior data over long periods, the system creates copies of what such data would reveal, dramatically reducing the time required while maintaining the statistical insights needed for high sensitivity.
Data Source
AI summary
The methods and systems described herein relate to conducting a controlled experiment using prediction of future user behavior. The method, executable on at least one server, comprises: collecting behavior data on two sets of users over a first period, wherein: the first set of users is exposed to a control; the second set of users is exposed to a treatment variant; and the behavior data relates to a performance parameter of the controlled experiment; based on a prediction model applied to the behavior data, calculating predicted values of the performance parameter for each user of the first set and the second set of users over a second period of time; and determining if a difference exists between the predicted values of the performance parameter for each user of the first set of users and the predicted values of the performance parameter for each user of the second set of users.