Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Consumption ability prediction method and apparatus, electronic device, and readable storage medium

A prediction method and ability technology, applied in the computer field, can solve problems such as low accuracy, rising or declining year by year, and achieve the effect of accurate consumption power value

Inactive Publication Date: 2018-02-16
BEIJING SANKUAI ONLINE TECH CO LTD
View PDF0 Cites 24 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, for the first method and the second method, the user’s most recent and certain consumption ability value is related to his specific consumption conditions, and may be determined as high consumption due to some reasons that he bought a product with a relatively high price Ability value users; for the third method, the user’s commodity purchase price may increase or decrease year by year within a few years, and the average value can only reflect an overall process
Therefore, it is less accurate to use the price of the user's latest purchase of goods, the price of a random purchase of goods, or the average price of historical purchased goods to determine the value of the user's spending power

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Consumption ability prediction method and apparatus, electronic device, and readable storage medium
  • Consumption ability prediction method and apparatus, electronic device, and readable storage medium
  • Consumption ability prediction method and apparatus, electronic device, and readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0025] refer to figure 1 , which shows a flow chart of a method for predicting consumption capacity in Embodiment 1 of the present invention, which may specifically include the following steps:

[0026] Step 101, acquiring statistical feature data and time series feature data for a target object from historical data of the target user.

[0027] For target users who need to predict their consumption ability value, the statistical feature data and time series feature data for the target object must be obtained from the target user's historical data.

[0028] Statistical characteristic data include one or any combination of the following data: historical consumption price parameters of target objects in any time period, historical browsing price parameters of target objects in any time period, historical consumption of non-target objects in any time period Price parameters, historical browsing price parameters of non-target objects in any period of time, user level, user active ...

Embodiment 2

[0044] refer to figure 2 , which shows a flow chart of a method for predicting consumption capacity in Embodiment 2 of the present invention, which may specifically include the following steps:

[0045] Step 201, from the sample user's historical data, obtain the sample user's statistical characteristic data, time series characteristic data and the actual consumption price for the target object.

[0046] For the sample users who have consumed the target object, from the historical data of the sample user, the statistical characteristic data, the time series characteristic data and the actual consumption price of the target object are obtained. The actual consumption price is the actual consumption price on the date specified by the sample user.

[0047] refer to image 3 , shows a schematic diagram of the hybrid neural network prediction model of the present invention.

[0048] image 3 Among them, X1, X2, ..., Xn-1, Xn represent the characteristic data of the input sampl...

Embodiment 3

[0085] refer to Figure 5 , shows a structural block diagram of an apparatus for predicting consumption ability according to Embodiment 3 of the present invention.

[0086] The consumption ability prediction device of the embodiment of the present invention comprises:

[0087] The first data acquisition module 501 is configured to acquire statistical characteristic data and time series characteristic data of the target object from historical data of the target user.

[0088] The consumption ability value determination module 502 is configured to determine the consumption ability value of the target user for the target object by using a preset hybrid neural network prediction model based on the statistical feature data and the time series feature data.

[0089] The consumption ability prediction device disclosed in the embodiment of the present invention obtains the statistical characteristic data and time series characteristic data of the target object from the historical dat...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The embodiment of the invention provides a consumption ability prediction method and apparatus, an electronic device, and a readable storage medium, and relates to the technical field of computers. The consumption ability prediction method includes the steps: acquiring the statistical characteristic data and the time sequence characteristic data of the target object from the historical data of thetarget user, based on the statistical characteristic data and the time sequence characteristic data, and utilizing the preset hybrid neural network prediction model to determine the consumption ability value of the target user for the target object. The consumption ability prediction method can solve the problem that the prior art utilizes the price of the commodity which is purchased by the userat the last time, the price of the commodity which is purchased randomly, or the price mean value of the commodities which are purchased in the history to determine the consumption ability value of the user, thus being lower in the accuracy. The consumption ability prediction method and apparatus combines with the time sequence characteristic data on the basis of the statistical characteristic data so as to be able to realize sequential dimension characteristic extraction of the historical data to enable the consumption ability value which is predicted by the hybrid neural network predictionmodel to be more accurate.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a consumption ability prediction method, device, electronic equipment and readable storage medium. Background technique [0002] The promotional business model using coupons has become popular. The use of coupons enables users to obtain discounts on product prices and additional services when purchasing products. In order to be able to target user groups with specified spending power values To issue coupons, it is necessary to determine the user's spending power value based on the user's historical consumption situation. [0003] At present, there are usually three ways to determine the value of the user's consumption ability. The first method: determine the value of the user's consumption ability according to the price of the user's latest purchase of goods; Consumption ability value; the third type: the consumption ability value is determined according to the average price o...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06N3/04
CPCG06Q30/0202G06Q30/0207G06Q30/0251G06N3/045G06Q30/0269G06Q30/0224G06Q30/0201G06N3/08G06N3/044G06N3/049G06N3/042G06N3/047
Inventor 徐俊李尚强翟艺涛王子伟
Owner BEIJING SANKUAI ONLINE TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com