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Application of Repeated Negative Sequence Pattern in Customer Purchasing Behavior Analysis

A technology of behavior analysis and negative sequence, applied in the direction of marketing, etc., can solve the problem that there is no repeated negative pattern mining method found

Active Publication Date: 2018-02-23
山东元竞信息科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these methods only consider the mining of repetitive positive sequential patterns, and we have not found any research on the mining of repetitive negative patterns.

Method used

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  • Application of Repeated Negative Sequence Pattern in Customer Purchasing Behavior Analysis
  • Application of Repeated Negative Sequence Pattern in Customer Purchasing Behavior Analysis
  • Application of Repeated Negative Sequence Pattern in Customer Purchasing Behavior Analysis

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specific Embodiment approach

[0095] The present invention will be described in detail below in conjunction with the examples, but not limited thereto.

Embodiment

[0097] An application of repeated negative sequence patterns in customer purchase behavior analysis, including the following steps:

[0098] (1) Define the number of times a negative sequence appears in a data sequence

[0099] MPS(ns) refers to the maximum positive subsequence of a negative sequence ns composed of items purchased by customers, which consists of all positive elements contained in ns in the original order; for example: a negative sequence Represents items that are not purchased, while c d represents items that are purchased. Its maximum positive subsequence is MPS(ns)=, especially, the maximum positive subsequence of a positive sequence is itself;

[0100] The number of times a negative sequence appears in a data sequence is determined by its left termination position; let ds=1 d 2 … d n > is a data sequence, for a negative sequence ns, if and make Then m is called the left end position, which is defined as LAE(ns,ds)=m, where m≥1 becau...

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Abstract

An application of repeated negative sequence patterns in the analysis of customer purchase behavior, and an efficient algorithm named e‑RNSP is proposed to mine repeated negative sequence patterns. And the excavated repeated positive sequence patterns and the number of repetitions in the data sequences containing them are saved correspondingly, and then the negative sequence candidate patterns are generated by the same method as e-NSP, and finally the repetition of the negative sequence candidate patterns is calculated by the formula support without having to scan the database multiple times. The e-RNSP is the first repetitive negative sequence pattern mining algorithm. The repetitive negative sequence pattern mined by this algorithm can more comprehensively analyze customer purchase behavior, so that the seller can predict future sales according to the current commodity sales situation. Merchandising.

Description

technical field [0001] The invention relates to the application of repeated negative sequence patterns in the analysis of customer purchase behavior, and belongs to the application technical field of repeated negative sequence patterns. Background technique [0002] With the advent of the Internet upsurge, the number of online shopping users continues to increase. For consumers, online shopping has become a brand new shopping experience and has gradually become an indispensable part of life. The Internet provides a new interactive shopping channel, and consumers get huge advantages: rich commodity information, overcoming geographical and time barriers, obtaining competitively priced commodities, personalization and customization of products, and more commodities selection, greater shopping convenience and more. In recent years, online shopping has grown explosively, with geometric growth every year. At the same time, many large-scale e-commerce websites, such as Amazon, Al...

Claims

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Application Information

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