Automatic Tokenization of Features Using Machine Learning

Aspects of the disclosure relate to generating recommendations for a user based on the customer's account information and the customer's activity on one or more media platforms using multiple machine learning (ML) models. A computing platform may determine a plurality of account features b...

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Bibliographische Detailangaben
Hauptverfasser:	Vemireddy, Vijaya L, Renckert, Jennifer, Oni, Shola, Nicholson, Diana
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS 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
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Beschreibung
Zusammenfassung:	Aspects of the disclosure relate to generating recommendations for a user based on the customer's account information and the customer's activity on one or more media platforms using multiple machine learning (ML) models. A computing platform may determine a plurality of account features based on the account information via a user ML model. The computing platform may determine a plurality of media features based on unstructured media data via a media ML model. A recommendation ML model generates tokens representing each of the plurality of account features and each of the plurality of media features in a fully connected graph structure. The recommendation ML model processes and outputs a recommendation score based on the tokens in the fully connected graph structure. A recommendation is generated by the computing platform based on the recommendation score.