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Product recommendation method and system for realizing customer classification based on integrated feature selection

A technology that integrates features and feature selection, applied in the field of artificial intelligence, can solve problems such as lack of pertinence, inability to achieve product promotion, and impact on customer experience, and achieve the effect of reducing impact

Pending Publication Date: 2020-09-04
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The traditional product push method is not targeted, and pushes the same product to all customers, which is blind and cannot achieve good product promotion, and there will be some meaningless data push, which seriously affects the customer experience

Method used

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  • Product recommendation method and system for realizing customer classification based on integrated feature selection
  • Product recommendation method and system for realizing customer classification based on integrated feature selection
  • Product recommendation method and system for realizing customer classification based on integrated feature selection

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] This embodiment provides a product recommendation method for realizing customer classification based on integrated feature selection;

[0047] Such as figure 1 As shown, the product recommendation method for customer classification based on integrated feature selection includes:

[0048] S101: Obtain historical product purchase data of customers to be recommended;

[0049] S102: Preprocessing the historical product purchase data of the customer to be recommended;

[0050] S103: Perform feature selection on the preprocessed data based on the set indicators, and select several features;

[0051] S104: Input the selected features into the pre-trained first neural network model, and output the categories of customers to be recommended;

[0052] S105: Input the category of the customer to be recommended into the pre-trained second neural network model, and output the recommended product of the customer to be recommended.

[0053] As one or more embodiments, in said S101,...

Embodiment 2

[0156] This embodiment provides a product recommendation system that realizes customer classification based on integrated feature selection;

[0157] A product recommendation system for customer classification based on integrated feature selection, including:

[0158] An acquisition module configured to: acquire historical product purchase data of customers to be recommended;

[0159] A preprocessing module configured to: preprocess the historical product purchase data of the customer to be recommended;

[0160] The feature selection module is configured to: perform feature selection on the preprocessed data based on a set index, and select several features;

[0161] The first output module is configured to: input the selected feature into the pre-trained first neural network model, and output the category of the customer to be recommended;

[0162] The second output module is configured to: input the category of the customer to be recommended into the pre-trained second neu...

Embodiment 3

[0167] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0168] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...

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Abstract

The invention discloses a product recommendation method and system for realizing customer classification based on integrated feature selection. The method comprises the steps of obtaining historical product purchase data of a to-be-recommended customer; preprocessing the historical product purchase data of the to-be-recommended customer; performing feature selection on the preprocessed data basedon set indexes, and selecting a plurality of features; inputting the selected features into a pre-trained first neural network model, and outputting the category of the to-be-recommended customer; andinputting the category of the to-be-recommended customer into a pre-trained second neural network model, and outputting a recommended product of the to-be-recommended customer.

Description

technical field [0001] The present disclosure relates to the technical field of artificial intelligence, in particular to a product recommendation method and system for realizing customer classification based on integrated feature selection. Background technique [0002] The statements in this section merely mention background art related to the present disclosure and do not necessarily constitute prior art. [0003] With the continuous development of the economy, the credit business of commercial banks is gradually increasing, and risk assessment or scoring of customers is an important process for every financial institution and bank. Through effective risk assessment, the financial risk borne by banks can be reduced to a great extent. The evaluation process can objectively classify the risks of customers based on their personal information and banking records. The high accuracy of classification helps to reduce risks in the banking and financial business process. The asp...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06Q30/02G06Q40/02G06N3/08
CPCG06Q30/0631G06Q30/0201G06N3/08G06Q40/03
Inventor 魏莹邓媛洁李玉军
Owner SHANDONG UNIV
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