Product recommendation method and device and electronic equipment

Product recommendation method and device and electronic equipment

The embodiment of the invention provides a product recommendation method and device and electronic equipment, and the method comprises the steps: determining a first target attribute number of target attribute information according to a preset corresponding relation between user attribute informatio...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: YE RONGGUANG, YU ZHANGJIAN, SUN HAIQING, HUANG QINGRONG, HUANG QIXIONG, NIU YUNNUO
Format: Patent
Sprache:chi ; 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
ELECTRIC DIGITAL DATA PROCESSING
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 embodiment of the invention provides a product recommendation method and device and electronic equipment, and the method comprises the steps: determining a first target attribute number of target attribute information according to a preset corresponding relation between user attribute information and a basic attribute number under the condition that the target attribute information of a target user is obtained; a target attribute set corresponding to the first target attribute number is determined based on the first target attribute number, the target attribute set comprises at least part of user attribute information with the association degree larger than a threshold value in the target attribute information, and normalization processing is conducted on the at least part of user attribute information, obtaining feature attribute information corresponding to the at least part of user attribute information; inputting the feature attribute information into a trained prediction model to obtain a product pre