Nearest Neighbor Matching for Automatic Bucket Assignment
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Solution Overview
Problem
Current methods for validating bucket assignments in user experience research computing systems, such as A/B testing, require significant time and effort, and existing approaches like A/A validation and ready-to-use A/A methodologies suffer from imbalance issues and user pool reduction, limiting the efficiency of experimentation.
Innovation Solution
The implementation of Nearest Neighbor Matching (NNM) for automatic bucket assignment, which assigns users to buckets based on hash values and standardized metric values, iteratively selecting users with the lowest pairwise distances to achieve balanced bucket assignments without the need for pre-testing validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If pre-testing validation (A/A validation) is conducted to validate bucket assignments, then bucket balance is improved, but time consumption and effort increase significantly
Solution Approach 1:
The patent applies preliminary action by computing pairwise distances between all users based on metric values before the actual bucket assignment process. This pre-computed distance information is stored and reused during assignment, eliminating the need for time-consuming A/A validation tests while ensuring balanced bucket assignments through nearest neighbor matching.
Solution Approach 2:
The patent creates a virtual representation of user characteristics through computed distance metrics that capture similarity between users. These distance copies are then used to guide bucket assignment without requiring actual A/A testing, reducing time consumption while maintaining assignment quality.
2Manufacturing precision
If ready-to-use A/A methodology is used to identify homogeneous user pool, then bucket balance is improved, but user pool size is reduced due to discarding users with extreme metric values
Solution Approach 1:
The patent transforms the approach from discarding users with extreme values to using those extreme values as anchors for nearest neighbor matching. By changing the parameter selection criterion from 'homogeneity within pool' to 'similarity between assigned users', the method retains all users in the pool while achieving balanced assignments through pairwise distance computation.
Solution Approach 2:
Instead of filtering out extreme users to create a homogeneous pool, the patent inverts the approach by using extreme users as reference points and finding their nearest neighbors. This inversion allows all users to be utilized while still achieving balanced bucket assignments through the nearest neighbor matching process.
3Manufacturing precision
If manual bucket assignment and validation process is used, then bucket balance can be achieved, but productivity and experimentation efficiency decrease
Solution Approach 1:
The patent implements self-service by creating an automated system that computes pairwise distances, performs nearest neighbor matching, and assigns users to buckets without manual intervention. The system serves itself by using pre-computed metric values and automatically generating balanced assignments, eliminating the need for manual validation while improving productivity.
Solution Approach 2:
The patent uses feedback from pre-computed metric values and pairwise distances to guide the bucket assignment process. By continuously referencing these computed values during assignment, the system automatically adjusts selections to maintain balance, improving experimentation efficiency without sacrificing assignment quality.
Data Source
AI summary
Techniques for assigning users to buckets for use in bucket experiments are disclosed. Disclosed systems and methods provide systems and methods for making automatic bucket assignments using Nearest Neighbor Matching (NNM). In one embodiment, an iterative approach is used in assigning users to buckets, such that in a given iteration selected users are assigned to a number of buckets, the selected users being an initial user selected from a pool of users and other users selected using pairwise distances associated with the initial user and the other users.