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Commodity recommendation method, device, storage medium and server

A recommendation method and product technology, applied in the direction of equipment, commerce, machine learning, etc., can solve problems such as lack of data foundation, failure to meet user preferences, blindness, etc., and achieve the effect of improving the purchase conversion rate

Active Publication Date: 2021-04-27
SHENZHEN LEXIN SOFTWARE TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, many users do not have the habit of evaluating the products they have purchased, which makes the method of recommending products by e-commerce companies blind without data basis. The products recommended by e-commerce companies cannot meet the preferences of users, and the purchase conversion rate is low.

Method used

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  • Commodity recommendation method, device, storage medium and server
  • Commodity recommendation method, device, storage medium and server
  • Commodity recommendation method, device, storage medium and server

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0059] figure 1 It is a flow chart of the product recommendation method provided in Embodiment 1 of the present invention. This embodiment is applicable to product recommendation situations. The method can be executed by the product recommendation device provided in the embodiment of the present invention. The device can be implemented by software and / or It can be realized by means of hardware and can be integrated in the server.

[0060] Such as figure 1 As shown, the recommended methods for the product include:

[0061] S110. Using machine learning means to train the implicit behavior data and historical purchase behavior results to obtain a training model.

[0062] Wherein, the result of historical purchase behavior may be that the user's final purchase result of a product is purchased or not purchased. Implicit behavior can be the user's behaviors such as browsing, adding to the shopping cart, purchasing, collecting, making an appointment, pre-ordering, and purchasing t...

Embodiment 2

[0072] figure 2 It is a flow chart of the product recommendation method provided in Embodiment 2 of the present invention. On the basis of the above-mentioned embodiments, this embodiment further optimizes the training model obtained by using machine learning means to train implicit behavior data and historical purchase behavior results.

[0073] Such as figure 2 As shown, the recommended methods for the product include:

[0074] S210. Obtain characteristic implicit behavior data of the user for at least one commodity, and obtain a result of the user's purchasing behavior for the commodity to form a sample set.

[0075] Wherein, the characteristic hidden behavior data of the user on the commodity includes: the number of times of at least one hidden behavior of the user among browsing, purchasing, adding to a shopping cart, collecting, making an appointment and pre-purchasing the commodity within at least one specific period of time. The specific time period can be 30 days...

Embodiment 3

[0083] image 3 It is a flow chart of the commodity recommendation method provided in the third embodiment of the present invention. On the basis of the above-mentioned embodiments, this embodiment further optimizes the training model obtained by using machine learning means to train implicit behavior data and historical purchase behavior results.

[0084] Such as image 3 As shown, the recommended methods for the product include:

[0085] S310. Obtain characteristic implicit behavior data of the user for at least one commodity, and obtain a result of the user's purchase behavior for the commodity to form a sample set.

[0086] Wherein, the characteristic hidden behavior data of the user on the commodity includes: the number of times of at least one hidden behavior of the user among browsing, purchasing, adding to a shopping cart, collecting, making an appointment and pre-purchasing the commodity within at least one specific period of time.

[0087] S320. Divide the sample ...

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Abstract

The embodiment of the invention discloses a commodity recommendation method, device, storage medium and server. The method includes: using machine learning means to train the implicit behavior data and historical purchase behavior results to obtain a training model; obtaining the implicit behavior data of the user to be recommended products, and obtaining the user's response to the user according to the training model. The degree of interest of the product to be recommended: according to the user's degree of interest in the product to be recommended, product recommendation is performed. By adopting the technical solutions provided by the embodiments of the present invention, it is possible to recommend products that are preferred by users, thereby improving the user's purchase conversion rate of recommended products.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of e-commerce commodity recommendation, and in particular, to a commodity recommendation method, device, storage medium, and server. Background technique [0002] At present, with the development of Internet of Things technology, the form of online shopping has been accepted by consumers. [0003] In the prior art, e-commerce recommends products for users, often based on obtained explicit data, such as users' likes and praises for products, to recommend products or similar products. However, many users do not have the habit of evaluating the products they have purchased, which makes the method of recommending products by e-commerce companies blind without data basis. The products recommended by e-commerce companies cannot meet the preferences of users, and the purchase conversion rate is low. Contents of the invention [0004] Embodiments of the present invention provide a commodity ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06G06Q30/02G06K9/62G06N20/00
CPCG06N20/00G06Q30/0201G06Q30/0631G06F18/214
Inventor 吴佳东
Owner SHENZHEN LEXIN SOFTWARE TECH CO LTD