Merchant recommendation method and device, electronic equipment and readable storage medium

A recommendation method, a merchant's technology, applied in the field of machine learning to achieve the effect of improving accuracy

Pending Publication Date: 2019-09-06
BEIJING SANKUAI ONLINE TECH CO LTD
View PDF0 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present disclosure provides a merchant recommendation method, device, electronic device and readable storage medium to partially or completely solve the above-mentioned problems related to the merchant recommendation process in the prior art

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
  • Merchant recommendation method and device, electronic equipment and readable storage medium
  • Merchant recommendation method and device, electronic equipment and readable storage medium
  • Merchant recommendation method and device, electronic equipment and readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0028] A recommendation method provided by an embodiment of the present disclosure is introduced in detail.

[0029] refer to figure 1 , shows a flowchart of steps of a recommendation method in an embodiment of the present disclosure.

[0030] Step 110, acquiring click sequence information of the target user.

[0031] In the embodiment of the present disclosure, in order to be able to recommend a target recommended merchant satisfying its current status to the target user, relevant reference information for determining the target recommended merchant needs to be obtained. Moreover, in practical applications, the merchants browsed by users through clicks and other means in the historical time can reflect their current needs to a certain extent. Therefore, in the embodiment of the present disclosure, the click sequence information of the target user may be obtained as reference information for determining the target recommended merchant.

[0032] Among them, the click sequenc...

Embodiment 2

[0050] A recommendation method provided by an embodiment of the present disclosure is introduced in detail.

[0051] refer to figure 2 , shows a flowchart of steps of a recommendation method in an embodiment of the present disclosure.

[0052] Step 210, acquiring characteristic information of the target user's clicks on merchants within a preset time period, and constructing click sequence information of the target user based on the characteristic information.

[0053] As mentioned above, in the embodiment of the present disclosure, the recommendation score of each candidate merchant is obtained based on the click sequence characteristics of the target user, so the acquisition of click sequence information is very important. In the embodiment of the present disclosure, in order to improve the accuracy and completeness of the click sequence information, the target user may click on the characteristic information of the merchant within a preset time period, and the click seque...

Embodiment 3

[0092] A merchant recommendation device provided by an embodiment of the present disclosure is introduced in detail.

[0093] refer to Figure 4 , shows a schematic structural diagram of a merchant recommendation device in an embodiment of the present disclosure.

[0094] The click sequence information acquisition module 310 is configured to acquire the click sequence information of the target user.

[0095]The recommendation score acquisition module 320 is used to obtain the recommendation score of each candidate merchant through a preset recommendation model according to the click sequence information; The combined model obtained after the network model is jointly trained, the two-layer cyclic neural network model includes a first-level cyclic neural network model and a second-level cyclic neural network model, and the input data of the second-level cyclic neural network model includes The output data of the first-level recurrent neural network model.

[0096] The target ...

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 invention discloses a merchant recommendation method and device, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring click sequence informationof a target user; according to the click sequence information, obtaining a recommendation score of each alternative merchant through a preset recommendation model; based on the recommendation scores of the alternative merchants, obtaining a target recommendation merchant of the target user and pushing the target recommendation merchant to the target user, wherein the recommendation model is a combined model obtained by carrying out combined training on a double-layer recurrent neural network model and a deep neural network model. Therefore, the technical problem that an existing recommendationmethod is poor in accuracy is solved. The beneficial effect of improving the accuracy of recommending merchants is achieved.

Description

technical field [0001] The present disclosure relates to the technical field of machine learning, and in particular to a merchant recommendation method, device, electronic equipment and readable storage medium. Background technique [0002] With the rapid development of the Internet and machine learning technology, more and more e-commerce platforms, etc. recommend personalized recommendation merchants that meet their needs to users through recommendation systems. The recommendation system generally sorts the candidate merchants for the current user to determine the final recommended merchant. [0003] At present, the application in machine learning model recommendation scenarios has achieved certain results, such as FNN (Feedforward Neural Network, feedforward neural network), RNN (Recurrent Neural Network, cyclic neural network) and DNN (Deep Neural Networks, deep neural network) Some effect has been achieved on the network. At present, there is also a method in the indu...

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): G06F16/9535G06N3/04G06Q30/06
CPCG06F16/9535G06Q30/0601G06N3/044G06N3/045
Inventor 钟超刘海文陈保密高玉龙
Owner BEIJING SANKUAI ONLINE TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap