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

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 ...

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