OPTIMIZING MEDIA REQUESTS WITH ENSEMBLE LEARNING

• Main Authors: Barnwal, Shubhranshu, Chladek, Matus, Povalyaev, Ivan, Jones, Zachary, Portman, Danny
• Format: Patent
• Language: English
• Published: 27.06.2024
• Subjects: CALCULATING; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The subject technology optimizes media requests to improve the efficiency and reduce the costs of online media campaigns. The request optimization system may implement one or more ensemble learning techniques that leverage multiple machine learning systems trained on different datasets. The request optimization system may use the ensemble learning techniques to generate optimized media requests that account for one or more campaign goals and minimize price inefficiencies incurred while purchasing placements in online media exchanges. In various embodiments, dynamic data including real time exchange and impression data may be collected and used to retrain one or more machine learning systems. Retaining the machine learning systems on dynamic data may improve the performance of optimized media requests determined by the retrained systems.