Machine learning based system and method of calculating a match score and mapping the match score to a level
Embodiments of the present invention are directed to a system and method of calculating a match score and mapping the match score to a level. Interested contractors in a list are ranked based on a rank score that is calculated for each of the interested contractor using a set of factors. The rank sc...
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creator | Kamil, Megan Emily Chiu, Kwan-Min Poon, Yuet Ping Rosati, Fabio |
description | Embodiments of the present invention are directed to a system and method of calculating a match score and mapping the match score to a level. Interested contractors in a list are ranked based on a rank score that is calculated for each of the interested contractor using a set of factors. The rank score is a combination of factor scores associated with the factors. A mapping engine, implementing a machine learning model, maps each of the interested contractors to one of at least two levels based on the set of factor scores, by comparing the set of factor scores to historical data collected to determine the likelihood of the contractor having that set being chosen by the client. Any interested contractor who has been mapped to the highest level is distinguished from others in the list. The mapping engine continuously learns from the historical data to improve future mappings. |
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Interested contractors in a list are ranked based on a rank score that is calculated for each of the interested contractor using a set of factors. The rank score is a combination of factor scores associated with the factors. A mapping engine, implementing a machine learning model, maps each of the interested contractors to one of at least two levels based on the set of factor scores, by comparing the set of factor scores to historical data collected to determine the likelihood of the contractor having that set being chosen by the client. Any interested contractor who has been mapped to the highest level is distinguished from others in the list. 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Interested contractors in a list are ranked based on a rank score that is calculated for each of the interested contractor using a set of factors. The rank score is a combination of factor scores associated with the factors. A mapping engine, implementing a machine learning model, maps each of the interested contractors to one of at least two levels based on the set of factor scores, by comparing the set of factor scores to historical data collected to determine the likelihood of the contractor having that set being chosen by the client. Any interested contractor who has been mapped to the highest level is distinguished from others in the list. The mapping engine continuously learns from the historical data to improve future mappings.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
title | Machine learning based system and method of calculating a match score and mapping the match score to a level |
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