Crowdsourced Contact Matching Algorithm with Dynamic Thresholds
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Solution Overview
Problem
Existing contact management systems face challenges in accurately identifying and managing unique contacts from a pool of merchant data, often resulting in duplication and errors due to inconsistencies in data attributes.
Innovation Solution
The system employs a match score algorithm to determine whether merchant data instances match, calculates error scores to identify probable errors, and uses user input to generate a training set to improve the algorithm's accuracy, updating the database and adjusting the match score threshold to optimize precision and recall ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a match score algorithm is used to automatically identify duplicate merchant data, then productivity is improved through automated processing, but measurement precision deteriorates due to errors in identifying unique contacts
Solution Approach 1:
The system implements a feedback mechanism where users can provide corrections on match results. These corrections are then fed back into the system to retrain and improve the match score algorithm, creating a continuous improvement loop that enhances measurement precision while maintaining automated processing
Solution Approach 2:
The system performs preliminary automated matching using the match score algorithm to identify potential duplicates before presenting them for user verification. This preliminary action filters out obvious cases and prepares data for more precise human review, improving overall measurement precision
2Measurement precision
If the match score threshold is set high to improve precision, then measurement precision is improved in identifying matches, but productivity deteriorates due to increased false negatives requiring manual review
Solution Approach 1:
The match score threshold is made dynamic rather than static. The system adjusts the threshold based on the specific characteristics of the data being processed and learns from user corrections to optimize the threshold setting, balancing precision and productivity adaptively
Solution Approach 2:
The system changes the match score threshold parameter based on different contexts and data types. By adjusting this parameter dynamically and learning from feedback, the system optimizes the balance between precision and productivity for different scenarios
3Measurement precision
If user input is solicited to correct errors in match results, then measurement precision is improved through crowdsourced correction, but device complexity increases due to additional user interaction components
Solution Approach 1:
The system enables users to self-correct errors in match results through an intuitive interface. Users can review and correct matching errors without requiring complex administrative procedures, improving precision while keeping the interaction simple
Solution Approach 2:
The system merges the automated algorithmic matching with human user correction capabilities into a unified process. The user interface is integrated seamlessly with the backend matching algorithm, allowing corrections to flow naturally into the matching process without adding significant complexity
Data Source
AI summary
Systems, apparatus, and methods for determining unique contacts from a collection or pool of merchant data are discussed herein. Some embodiments may provide for an apparatus including circuitry configured to determine programmatic match results indicating whether different instances of merchant data match (e.g., describe the same contact). The circuitry may further determine probabilities of precision or recall errors with the programmatic match results. Programmatic match results having a high probability of error may be annotated by a user to generate user match results. The user match results may be used to generate a more reliable contacts database including unique contacts, as well as to train and/or update the match scoring algorithm. As such, the accuracy of machine-implemented binary classification is improved.