Control Group Formation With Profile Vectors for Exposure Measurement
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Solution Overview
Problem
Existing methods struggle to accurately form a control group that matches the characteristics of an exposed group to which predetermined data has been disseminated, leading to potential discrepancies and suboptimal assessment of the data's effect on end users.
Innovation Solution
A measurement platform with a control group module and survey module that iteratively determines a control group by selecting user identifiers with similar features to the exposed group, using binary feature vectors and one-hot encoding, to ensure conformity and minimize differences, and sends requests for responses to assess the data's effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a control group is formed using existing methods, then the process is simple and quick, but the control group does not accurately match the characteristics of the exposed group
Solution Approach 1:
User profiles are pre-stored in the profile storage with comprehensive feature sets before the A/B test begins. This preliminary organization of data enables rapid and accurate control group formation without requiring complex real-time processing during the experiment
Solution Approach 2:
The patent replaces manual or simple random control group formation methods with an automated computational system that uses processing circuitry to compare binary feature vectors and iteratively select matching users, achieving high precision through algorithmic processing rather than mechanical selection
2Measurement precision
If the control group is formed iteratively with continuous correction, then the conformity between groups is improved, but the processing time and computational resources increase
Solution Approach 1:
The system evaluates multiple features for each user profile but selectively focuses computational effort on the most discriminative features that significantly impact group conformity. This partial evaluation approach achieves high precision without requiring exhaustive analysis of all possible features
Solution Approach 2:
The patent creates binary feature vector representations of user profiles that are computational copies of the original profile data. These simplified vector copies enable rapid comparison and iterative selection without repeatedly accessing and processing the full original profiles, reducing computational overhead
3Measurement precision
If binary feature vectors and one-hot encoding are used to represent user profiles, then the feature comparison accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The patent transforms user profile data from raw formats into standardized binary feature vectors using one-hot encoding. This parameter transformation converts categorical and numerical data into a uniform binary representation that enables efficient bitwise operations and accurate feature comparison without requiring complex processing logic
Data Source
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AI summary
In an example, a measurement platform comprises a memory and processing circuitry configured to provide a control group module and a survey module. The memory comprises profile storage configured to store a plurality of end user identifiers associated with end user profiles comprising a set of features associated with the end user. The control group module is configured to receive exposure notifications indicating that predetermined data has been exposed to end users, to determine user profiles associated with exposed end user identifiers and to determine a control group of user identifiers associated with profiles comprising similar features to exposed end user profiles but not comprising the exposed end user identifiers. The survey module is configured to send a request for a response to end user devices associated with exposed and control group end user identifiers, compare responses received from exposed end users with responses received from control group end users and determine, based thereon, an indication of an effect of the predetermined data on the exposed end users.