A household product recommendation method, client and server

Abstract

The embodiment of the present application discloses a home product recommendation method, a client and a server, wherein the method provides an image sample library associated with the home, and the image sample library includes scene image samples and scenes associated with the scene image samples Commodity image sample, the household product represented by the commodity image sample is located in the scene represented by the scene image sample, wherein the scene image sample has a scene label, the method includes: receiving the indoor shot image sent by the client, and based on the pre-trained neural network , identify the scene label of the captured image from the captured image; determine the target scene image sample that matches the captured image from the image sample library according to the recognized scene label of the captured image; associate the target scene image sample with the commodity image sample The represented household items are fed back to the client. The technical solution provided by the application can improve the user's home shopping experience while reducing the cost of home shopping.

Description

technical field [0001] The present application relates to the field of Internet technologies, and in particular to a method for recommending household items, 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 decoration industry, when purchasing home furnishing products online, users often need to judge whether the selected home furnishing products match their real home environment reasonably. [0003] At present, in order to select home furnishing products that match the real home environment, users often need to make an appointment with a designer to come to the door, so that the designer can provide some suggestions on matching home products or furniture customization according to the real home environment of the user's home. In this way, the user Then, according to the suggestions given by the designer, you...

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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