GENERALIZED ADDITIVE MACHINE-LEARNED MODELS FOR COMPUTERIZED PREDICTIONS

In an example, predictions/recommendations using machine learned models are made even more accurate by using three models instead of a single Generalized Linear Mixed (GLMix) model. Specifically, rather than having a single GLMix model with different coefficients for users and items, three separate...

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Bibliographische Detailangaben
Hauptverfasser:	Shelkovnykov, Alex, Ma, Yiming, Fleming, Josh, Chen, Bee-Chung, Agarwal, Deepak
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:	In an example, predictions/recommendations using machine learned models are made even more accurate by using three models instead of a single Generalized Linear Mixed (GLMix) model. Specifically, rather than having a single GLMix model with different coefficients for users and items, three separate models are used and then combined. Each of these models has different granularities and dimensions. A global model models the similarity between user attributes (e.g., from the member profile or activity history) and item attributes. A per-user model models user attributes and activity history. A per-item model models item attributes and activity history. Such a model may be termed a Generalized Additive Mixed Effect (GAME) model.