ENTITY REPRESENTATION LEARNING FOR IMPROVING DIGITAL CONTENT RECOMMENDATIONS
A machine is configured to improve content recommendations. For example, the machine accesses a first score representing an affinity between a job description and a member profile. The first score is generated based on a first embedding that represents the job description, and includes a feature tha...
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Main Authors | , |
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Format | Patent |
Language | English French German |
Published |
02.10.2019
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Subjects | |
Online Access | Get full text |
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Summary: | A machine is configured to improve content recommendations. For example, the machine accesses a first score representing an affinity between a job description and a member profile. The first score is generated based on a first embedding that represents the job description, and includes a feature that identifies an organization associated with the job description, and a second embedding that represents the member profile. The machine, based on the first score exceeding a first threshold value, causes a display of a recommendation of the job description in a user interface. The machine, based on an indication of selection of the job description, generates a third embedding that represents an article associated with the organization. The machine generates a second score that represents a member profile-job affinity, and, based on the second score exceeding a second threshold value, causes a display of a recommendation of the article in the user interface. |
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Bibliography: | Application Number: EP20190166178 |