Personalized commodity recommendation method based on machine vision and improved neural network

Abstract

The invention discloses a personalized commodity recommendation method based on machine vision and an improved neural network, and the method comprises the steps: firstly obtaining a face image through a camera, training a face recognition model through a deep neural network according to the obtained image, and carrying out the recognition of a customer IP through the trained model; secondly, capturing behavior data of customers in the shopping process through a camera and a human body posture recognition technology, obtaining interests and hobbies of the customers, and combining personal attribute information of the customers to serve as feature vectors of the customers; finally, inputting the feature vectors of the customers and the features of the commodities into a personalized recommendation system. The requirements of quickly and accurately recommending the commodities meeting the preferences of the customers to the customers can be met, and the labor cost is reduced.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a personalized product recommendation method based on machine vision and improved neural network. Background technique [0002] In recent years, with the rapid development of e-commerce, people's traditional shopping habits have been greatly impacted. As a result, the market environment has become more complicated, and offline sales are showing a trend of sluggishness. Faced with the increasing demand for efficient and fast services from the general public, how to conduct accurate and effective sales based on consumers' purchase records, shopping preferences, and consumption levels? Product recommendation has become the focus of many merchants. With the continuous development of digital image processing technology, the application scenarios of face recognition systems have become more extensive, resulting in the emergence of new retail models such as ...

AI Technical Summary

Problems solved by technology

[0003] Modern society is in a fast-paced way of life, so it is bound to be the pursuit of people to recommend services quickly and well. Traditional recommendations are completely artificial. For a large amount of product information, customers do not have the energy to browse completely, and sales Due to the lack of understanding of customers, it is difficult for personnel to produce effective recommendations, which is time-consuming and labor-intensive and difficult to meet customer needs

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