公开(公告)号：US10572929B2

公开(公告)日：2020-02-25

申请号：US15853866

申请日：2017-12-25

Applicant: Industrial Technology Research Institute

Inventor： Cheng-Liang Lin ,  Jie-Sin Li ,  En-Tzu Wang ,  Jyun-Tang Huang ,  Tsung-Wen Tso

IPC: G06Q30/00 ,  G06Q30/06 ,  G06Q30/02 ,  G06F17/18 ,  G06N5/04 ,  G06F16/2457

Abstract: A decision factors analyzing device and a decision factors analyzing device for analyzing a plurality of decision factors which cause a product of a product type to be purchased are provided. The method includes identifying a plurality of product sequences corresponding to the product type from a plurality of browse history data and a plurality of purchase history data corresponding to a plurality of consumers of a consumer database, wherein each of the product sequences includes a unpurchased product and a purchased product; obtaining a feature sequence according to the produce sequences and a plurality of product information; training a regression model corresponding to the product type according to K decision factors of the feature sequence to obtain an optimized regression model, and obtaining K decision values respectively corresponding to the K decision factors according to the optimized regression model to generate a decision factor sequence corresponding to the product type.

公开(公告)号：US20190164213A1

公开(公告)日：2019-05-30

申请号：US15853866

申请日：2017-12-25

Applicant: Industrial Technology Research Institute

Inventor： Cheng-Liang Lin ,  Jie-Sin Li ,  En-Tzu Wang ,  Jyun-Tang Huang ,  Tsung-Wen Tso

IPC: G06Q30/06 ,  G06Q30/02 ,  G06F17/30 ,  G06N5/04 ,  G06F17/18

Abstract: A decision factors analyzing device and a decision factors analyzing device for analyzing a plurality of decision factors which cause a product of a product type to be purchased are provided. The method includes identifying a plurality of product sequences corresponding to the product type from a plurality of browse history data and a plurality of purchase history data corresponding to a plurality of consumers of a consumer database, wherein each of the product sequences includes a unpurchased product and a purchased product; obtaining a feature sequence according to the produce sequences and a plurality of product information; training a regression model corresponding to the product type according to K decision factors of the feature sequence to obtain an optimized regression model, and obtaining K decision values respectively corresponding to the K decision factors according to the optimized regression model to generate a decision factor sequence corresponding to the product type.