Invention Grant
- Patent Title: Machine learning methods and systems for predicting online user interactions
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Application No.: US15704320Application Date: 2017-09-14
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Publication No.: US10943184B2Publication Date: 2021-03-09
- Inventor: Rodrigo Acuna Agost , Alejandro Ricardo Mottini D'Oliveira , David Renaudie
- Applicant: Amadeus S.A.S.
- Applicant Address: FR Biot
- Assignee: Amadeus S.A.S.
- Current Assignee: Amadeus S.A.S.
- Current Assignee Address: FR Biot
- Agency: Thompson Hine LLP
- Main IPC: G06Q10/00
- IPC: G06Q10/00 ; G06Q30/02 ; G06N20/00 ; G06N5/02

Abstract:
Methods and computing apparatus for retrieving records relating to content placement events and records relating to user interaction events. A set of enriched training feature vectors is computed from raw feature values, and used with interaction event tags to train a machine learning model. Information is received relating to an online content placement slot and information is received relating to a user to whom content within the online content placement slot will be displayed. An enriched estimation feature vector is computed based upon a content item selected for placement within the online content placement slot, the information relating to the user, and the information relating to the online content placement slot. A machine learning model is executed to determine an estimate of likelihood of the user interacting with the selected content item, based upon the enriched estimation feature vector.
Public/Granted literature
- US20190080260A1 MACHINE LEARNING METHODS AND SYSTEMS FOR PREDICTING ONLINE USER INTERACTIONS Public/Granted day:2019-03-14
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