Unlock instant, AI-driven research and patent intelligence for your innovation.

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

A recommendation method and recommendation device technology are applied in the field of storage media and servers, devices, and commodity recommendation methods, which can solve the problems of lack of data foundation, blindness, and low purchase conversion rate, and achieve the effect of improving purchase conversion rate.

Active Publication Date: 2018-02-16
SHENZHEN LEXIN SOFTWARE TECH CO LTD
View PDF4 Cites 38 Cited by
  • 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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Commodity recommendation method, commodity recommendation device, storage medium and server
  • Commodity recommendation method, commodity recommendation device, storage medium and server
  • Commodity recommendation method, commodity recommendation 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 ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The embodiment of the invention discloses a commodity recommendation method, a commodity recommendation device, a storage medium and a server. The method comprises the following steps of training recessive behavior data and historical purchasing behavior results by utilizing the machine learning means so as to obtain a training model; acquiring the recessive behavior data of a user on to-be-recommended commodities, and obtaining the interest degrees of the user on the to-be-recommended commodities according to the training model; according to the interest degrees of the user on the to-be-recommended commodities, recommending a commodity. According to the technical scheme provided by the embodiment of the invention, commodities are recommended according to user preferences. Therefore, the purchase conversion rate of the user on recommended commodities is improved.

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06Q30/02G06K9/62G06N99/00
CPCG06N20/00G06Q30/0201G06Q30/0631G06F18/214
Inventor 吴佳东
Owner SHENZHEN LEXIN SOFTWARE TECH CO LTD