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Intelligent shopping recommendation method based on big data

A recommendation method and big data technology, applied in the field of big data, can solve problems such as potential safety hazards, time-consuming, inability to see products intuitively, etc., and achieve the effect of broadening the scope of application and correcting inconsistent measurement standards

Pending Publication Date: 2021-01-08
朱丽勤
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the continuous development of e-commerce, more and more people are used to online shopping, and the number of shopping in physical stores in shopping malls is less and less. Although online shopping is relatively more convenient, users can do it without leaving home. There are certain risks in buying commodities: firstly, users cannot actually and intuitively see the commodities when shopping online. Exposing consumers' personal information in an open network environment for a long time poses security risks, while shopping in physical stores allows users to see the authenticity of the products and have an intuitive feeling for the products, making it easier for users to choose the right products
[0003] However, shopping in physical stores also has certain disadvantages: First, there are more choices for shopping in physical stores in shopping malls. For users with difficulty in choosing, it takes a long time to buy the products they are satisfied with. It can help such users to buy products that suit them more quickly and easily; second, when shopping in a large physical store in a large shopping mall, after determining what they need to buy, how to buy these products more effortlessly and faster become a problem that needs to be solved, which requires systematically planning a reasonable path to help users shop

Method used

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  • Intelligent shopping recommendation method based on big data
  • Intelligent shopping recommendation method based on big data
  • Intelligent shopping recommendation method based on big data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0063] Example 1: Set the vector coordinates of the desired product to (1, 2), and the vector coordinates of product 1 , the vector coordinates of commodity 2 , according to the formula Calculate the similarity (that is, the cosine value of the included angle) between commodity 1 and the desired commodity as 0.984, the calculated similarity between product 2 and the required product (ie, the cosine value of the included angle) is 0.978<0.984, so product 1 is more similar to the desired product, and product 1 is recommended first, followed by product 2.

Embodiment 2

[0064] Example 2: There are two items to be purchased, namely n=2, which are item 1 and item 2 respectively. The entrance coordinates of the shopping mall are set to (0, 0, 0), and the exit coordinates of the shopping mall are (a, b, 0) = (5, 0, 0), the position coordinates of commodity 1 are (a 1 , b 1 , c 1 ) = (1, 1, 1), the coordinates of commodity 2 are (a 2 , b 2 , c 2 ) = (3, 4, 3), there are two routes: route 1: entrance Commodity 1 Commodity 2 Exit; Route 2: Entry Commodity 2 Commodity 1 export, according to the formula Calculate the total distance of route 1 as 11.24, according to the formula Calculate the total distance of route 2 as 14.20>11.24, so the total distance of route 1 is less than the total distance of route 2, first go to buy product 1, then buy product 2, the distance is the shortest, choose route 1.

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Abstract

The invention discloses an intelligent shopping recommendation method based on big data. The method comprises the following steps: identifying keywords through a voice identification module to determine the identity of a user and the category of a commodity to be purchased; modeling a shopping mall, positioning the position of a commodity through a GPS module, and calling the position informationof the required commodity through a database, so that the purpose of planning an optimal path for the commodity purchased by a user is achieved; and recommending similar commodities of the required commodities to the user according to the prices of the commodities and the similarity between the commodities. Special information of the recommended commodities stored in the data base is transmitted to the display terminal through a WIFI module for clients to select, and each module performs connection communication through the IO port of a single chip microcomputer. According to the invention, the trouble of difficulty in selection of the user during shopping is reduced, the shopping route is planned for the user, and the required travel distance is reduced. Therefore, the user can keep a relaxed and pleasant mood in the shopping process.

Description

technical field [0001] The invention relates to the field of big data, in particular to an intelligent shopping recommendation method based on big data. Background technique [0002] With the continuous development of e-commerce, more and more people are used to online shopping, and the number of shopping in physical stores in shopping malls is less and less. Although online shopping is relatively more convenient, users can do it without leaving home. There are certain risks in buying commodities: firstly, users cannot actually and intuitively see the commodities when shopping online. Exposing consumers' personal information in an open network environment for a long time poses security risks, while shopping in physical stores allows users to see the authenticity of the products and have an intuitive feeling about the products, making it easier for users to choose the right products. [0003] However, shopping in physical stores also has certain disadvantages: First, there a...

Claims

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Application Information

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IPC IPC(8): G06Q30/06G06Q10/04G06F16/9535G06F16/9537G01S19/42
CPCG06Q30/0631G06Q30/0639G06Q30/0643G06Q10/047G06F16/9535G06F16/9537G01S19/42
Inventor 朱丽勤
Owner 朱丽勤
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