Artificial intelligence system for balancing relevance and diversity of network-accessible content

Artificial intelligence system for balancing relevance and diversity of network-accessible content

The present disclosure is directed to training, and providing recommendations via, a machine learning model architected to balance relevance and diversity of sets of recommendations. For example, a neural network can be provided with user profile features and can output probabilities for each of a n...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Sandler, Samuel Theodore, Mohan, Karthik
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:The present disclosure is directed to training, and providing recommendations via, a machine learning model architected to balance relevance and diversity of sets of recommendations. For example, a neural network can be provided with user profile features and can output probabilities for each of a number of recommendations. This can be converted into a ranked list of recommendations. The ranked list of recommendations is provided to a diversity model that maximizes an optimization objective having a first objective that quantifies relevance of a recommendation and a second objective that measures diversity of a set of recommendations. The output of the diversity model is a set of recommendations that have both high relevance and high diversity.