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E-commerce product recommendation method based on big data and multiple tags

A technology for e-commerce and product recommendation, applied in the field of big data, can solve problems such as lack of recommended matching products, and achieve the effect of improving shopping efficiency

Active Publication Date: 2021-10-08
苏州细无声信息科技服务有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In order to improve the user's shopping experience, the existing technology can recommend similar products to the user according to the user's history of purchasing products, but in real life, when the user has purchased a product, what he needs is actually a matching product. Technology that recommends products to go with purchased products is missing from

Method used

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  • E-commerce product recommendation method based on big data and multiple tags

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Embodiment Construction

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0033] see figure 1 , the present invention provides a technical solution: a method for recommending e-commerce commodities based on big data and multiple tags, the recommendation method comprising the following steps:

[0034] Step S1: collect the search keyword of the product input by the user as the first keyword, and collect the keywords of each product purchased by the user in the history as the second keyword;

[0035] Step S2: Determine whether there i...

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Abstract

The invention discloses a method for recommending e-commerce commodities based on big data and multiple tags. The recommendation method includes the following steps: Step S1: Collect the search keyword of the commodity input by the user as the first keyword, and collect the purchased products in the user's history. The keyword of each product is the second keyword; step S2: judge whether there is a second keyword and the first keyword as matching keywords, if there is a second keyword and the first keyword as matching keywords, set the last The product corresponding to the second keyword is the product to be matched, and the product searched by the user inputting the first keyword is the search product, and the search product is marked according to the sales page content of the product to be matched and the search product; Step S3: from the marked search The products selected from the products are recommended to users first.

Description

technical field [0001] The invention relates to the field of big data technology, in particular to a method for recommending e-commerce commodities based on big data and multiple labels. Background technique [0002] With the development of Internet technology, online shopping has become an important way of shopping in people's life. Online shopping can realize shopping at home, and the purchase of goods is not limited by time and place, and can obtain more product information, and can buy products that are not available in the local area. In order to improve the user's shopping experience, the existing technology can recommend similar products to the user according to the user's history of purchasing products, but in real life, when the user has purchased a product, what he needs is actually a matching product. Technology to recommend products that go with purchased products is missing from . Contents of the invention [0003] The purpose of the present invention is to ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06G06F16/9535G06F16/9532
CPCG06Q30/0631G06F16/9532G06F16/9535
Inventor 韦亚洲赵磊
Owner 苏州细无声信息科技服务有限公司
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