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A product recommendation system and its working method based on decision-making high-utility negative sequence rule mining

A product recommendation and working method technology, applied in business, data processing applications, special data processing applications, etc., can solve conflicts, difficulties, and problems that do not meet customer needs

Active Publication Date: 2021-06-01
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

However, one of the shortcomings is that sometimes the recommended products provided to users obviously do not meet the needs of customers
Although the existing product recommendation methods can obtain a lot of information, a large part of the information is redundant or even contradictory. How to filter out these useless information is very difficult; in addition, how to take advantage of offline stores, Collecting relevant information of customers, analyzing it efficiently, and then obtaining recommendation information that can be directly used for decision-making is a technical problem that needs to be overcome

Method used

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  • A product recommendation system and its working method based on decision-making high-utility negative sequence rule mining
  • A product recommendation system and its working method based on decision-making high-utility negative sequence rule mining
  • A product recommendation system and its working method based on decision-making high-utility negative sequence rule mining

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0071] A product recommendation system based on decision-making high-utility negative sequence rule mining, such as figure 1 As shown in , it includes an information collection module, a product recommendation module, and a product sales module that are sequentially connected through transmission network communication;

[0072] The information collection module includes an information extraction module and a first information transmission module connected in sequence; the information extraction module is used to: extract and save customer behavior data in real time, and the customer behavior data includes customer ID, face mark, gender, age, time stamp , the logo of the commodity browsed by the customer; the first information transmission module is used to: transmit the behavior data of the customer to the commodity recommendation module through the transmission network;

[0073] The commodity recommendation module includes an information processing module, an information anal...

Embodiment 2

[0078] The working method of the product recommendation system based on the decision-making high-utility negative sequence rule mining described in embodiment 1 includes the following steps:

[0079] (1) The information extraction module extracts and saves the customer's behavior data in real time. The customer's behavior data includes customer ID, face mark, gender, age, time stamp, and the product mark that the customer browses; among them, face marks such as whether to wear glasses, Eye coordinates.

[0080] (2) The first information transmission module transmits the customer's behavior data extracted by the step (1) information collection module to the product recommendation module through the transmission network;

[0081] (3) The information processing module performs data cleaning on the collected customer behavior data, and performs data classification on the data after data cleaning;

[0082] (4) According to the processing results of the information processing modul...

Embodiment 3

[0087] According to the working method of the product recommendation system based on the decision-making high-utility negative sequence rule mining described in embodiment 2, the steps are as follows:

[0088] In this embodiment, the shopping data records of snacks sold in an offline store of a shopping mall are used as experimental data. Table 1 and Table 2 are the partial results of preprocessing customer shopping behavior data into utility sequence database and utility table respectively.

[0089] Table 1

[0090] Customer ID shopping sequence C1

C2

C3

… …

[0091] Table 2

[0092] item Walnut Pecans dried strawberries Spicy Dried Tofu dried mango Unit utility (yuan / 1kg) 166.9 146 150 113 216

[0093] step (3), because real-world data are generally incomplete, noisy and inconsistent. When collecting customer behavior data through the information collection module, there may be missing values, ...

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Abstract

The present invention relates to a product recommendation system and its working method based on high-efficiency negative sequence rule mining that can be decided. It includes an information collection module, a product recommendation module, and a product sales module connected in sequence; real-time extraction and storage of customer behavior data, transmission To the product recommendation module; data cleaning and data classification of the collected customer behavior data; analysis and prediction of customer shopping behavior; establishment of a shopping behavior sequence corresponding to the customer ID, the shopping behavior of customers with the same gender and in the same age range Behavioral data constitutes a sequence database; the sequence database is mined to obtain the high-utility negative sequence rules that meet the requirements and can be decided, that is, the product recommendation that meets the customer; the present invention not only considers the statistical correlation between things, but also Taking the semantic meaning between them into account, many useless rules can be deleted, and more meaningful rules that can be directly used for decision-making can be obtained.

Description

technical field [0001] The invention relates to a product recommendation system and a working method based on mining of decision-making high-utility negative sequence rules, and belongs to the application technical field of decision-making high-utility negative sequence rules. Background technique [0002] The popularization of Internet technology has promoted the rapid development of online e-commerce. The advantage of online e-commerce is that it can identify different users based on user accounts, browser cookies, etc., and then recommend products to users based on their historical browsing and purchase records. However, one of the shortcomings is that sometimes the recommended products provided to users obviously do not meet the needs of customers. In addition, offline stores are still an important way to sell goods, but due to their lack of intelligence, it is impossible to achieve product recommendations and corresponding user experience similar to online e-commerce. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/2458G06F16/9535G06K9/62G06Q30/06
CPCG06F16/2465G06F16/9535G06Q30/0631G06F18/24G06Q30/0282
Inventor 董祥军张孟姣
Owner 山东元竞信息科技有限公司
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