Application of Positive and Negative Sequential Pattern Screening Method in Analysis of Customer Purchase Behavior

A sequential pattern and behavior analysis technology, applied in marketing and other directions, can solve the problem of not finding positive and negative sequential patterns, and achieve the effect of improving cross-selling ability, increasing transaction opportunities, and improving loyalty

Active Publication Date: 2018-04-27
山东元竞信息科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] While there are many papers discussing feasible knowledge discovery and selecting feasible patterns / rules or methods of interest in association rule mining, no papers have been found so far on how to select feasible positive and negative sequential patterns

Method used

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  • Application of Positive and Negative Sequential Pattern Screening Method in Analysis of Customer Purchase Behavior
  • Application of Positive and Negative Sequential Pattern Screening Method in Analysis of Customer Purchase Behavior
  • Application of Positive and Negative Sequential Pattern Screening Method in Analysis of Customer Purchase Behavior

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Embodiment 1

[0061] The application of positive and negative sequence pattern screening method in customer purchasing behavior analysis includes the following steps:

[0062] (1) Use the correlation coefficient function to measure the relationship between commodities and commodities:

[0063] A commodity purchased by a customer each time is a separate item, and all commodities purchased by the customer at a specific time point are one element. element; ρ represents the correlation coefficient between any two elements in a sequence:

[0064] If ρ>0, then the above two elements are positively correlated; the more products you buy at one time, the more products you buy at another time;

[0065] If ρ=0, then the above two elements have no correlation; the purchase behavior of the commodity purchased once and the commodity purchased in the other time are independent of each other, and the two purchase behaviors do not affect each other;

[0066] If ρ<0, then the above two elements...

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Abstract

The invention relates to an application of a positive and negative sequence mode screening method in customer purchasing behavior analysis and provides an efficient algorithm named as SAP to select feasible positive and negative sequence modes. The main idea of the algorithm is to excavate all positive and negative sequence modes through the e-NSP method and screen each sequence mode, and finally feasible positive and negative sequence modes are selected out. The method is applied in the customer purchasing behavior analysis process, commodities purchased by customers most within a certain period are screened out through minimum support, and commodities most related to purchasing of customers within the period are also screened out through a correlation coefficient. In this way, when customers purchase commodities, the method can be used for recommending some commodities which may be bought by other customers and most related with the purchased commodities, and therefore purchasing chances of the customers are increased. Network browsers are converted into purchasers, the cross-selling capacity is improved, loyalty of the customers is improved, and the economic benefits of a website are increased.

Description

technical field [0001] The invention relates to the application of a positive and negative sequence pattern screening method in customer purchasing behavior analysis, and belongs to the technical field of sequence pattern mining applications. Background technique [0002] With the popularization of the Internet and the development of e-commerce business, online shopping has become one of the main shopping methods for people. We only need to operate the Internet to get more satisfactory products without leaving home. However, with the surge in the amount of information, online shopping has become complicated and time-consuming, and many large e-commerce sites, such as Amazon, Alibaba's Taobao and Tmall Mall, JD.com, etc. have accumulated a large amount of customer transaction data . How to make full use of these data, obtain customers' shopping patterns, and make personalized product recommendations for customers, so as to better improve the service quality and economic ben...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/02
Inventor 董祥军徐田田
Owner 山东元竞信息科技有限公司
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