Household commodity recommendation method, client and server

Abstract

The embodiment of the invention discloses a household commodity recommendation method, a client and a server. The method comprises the following steps: providing an image sample library associated with home furnishing, wherein the image sample library comprises a scene image sample and a commodity image sample associated with the scene image sample, wherein the household commodity represented by the commodity image sample is located in a scene represented by the scene image sample, the scene image sample is provided with a scene label. The method comprises the following steps: receiving an indoor shot image sent by a client, and identifying the scene label of the shot image from the shot image based on a pre-trained neural network; determining a target scene image sample matched with the shot image from an image sample library according to the identified scene label of the shot image; and feeding back the household commodity represented by the commodity image sample associated with thetarget scene image sample to the client. According to the technical scheme provided by the invention, the home shopping experience of the user can be improved while the home shopping cost is reduced.

Description

Technical field [0001] This application relates to the field of Internet technology, and in particular to a method for recommending household goods, a client and a server. Background technique [0002] With the continuous development of Internet technology, more and more people choose to shop online. For some traditional industries, such as the furniture and home improvement industry, when purchasing home furnishing products online, users often need to judge whether the selected home furnishing products match the real home environment of their homes. [0003] At present, in order to select home furnishing products that are reasonably compatible with the real home environment, users often need to make an appointment for the designer to visit, so that the designer can provide some home furnishing product matching suggestions or furniture customization suggestions based on the user’s real home environment. According to the suggestions given by the designer, you can buy home furnishin...

AI Technical Summary

Problems solved by technology

However, this way of shopping for home furnishing products requires offline manual services to purchase suitable home furnishing products online. The purchasing process is cumbersome and the cost is high, which reduces the user's home shopping experience.

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