Recommendation information generation method and device, electronic equipment and storage medium

Abstract

The invention discloses a recommendation information generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining an image of a user in a shop;analyzing the images of the users in the store, and determining companions; matching the face images of the companions with each face image in a face library to obtain historical consumption behaviordata of the companions; and generating corresponding push information according to the historical consumption behavior data of the companions. According to the method, the user group for analyzing thehistorical consumption behavior data of the user is expanded, and the quantity and the value of the user data are improved, so that the obtained push information can better meet the actual requirements of the push main body (the main body for receiving the push information); meanwhile, the user data can be analyzed from multiple dimensions, so that the richness of the obtained analysis result canbe improved, the audience range of the push main body to which the analysis result is applicable is expanded, and the utilization rate of the user data is increased.

Description

technical field [0001] The embodiments of the present application relate to the technical field of data processing, and in particular to a method, device, electronic device, and storage medium for generating recommendation information. Background technique [0002] At present, in order to improve operating income, offline stores usually analyze the historical consumption behavior data of all users who visit the store, and then adjust their operating strategies based on the analysis results. Therefore, it is particularly important to obtain user data with analytical value. [0003] In related technologies, since users can place an order and checkout by scanning the QR code, or manually checkout at the cash register, most stores can obtain the data of some of the users who come to the store through the checkout person, and the It is impossible to obtain the data of other users who consume in the store, which makes the user data obtained by the store have great limitations, an...

AI Technical Summary

Problems solved by technology

[0003] In related technologies, since users can place an order and checkout by scanning the QR code, or manually checkout at the cash register, most stores can obtain the data of some of the users who come to the store through the checkout person, and the It is impossible to obtain the data of other users who consume in the store, which makes the user data obtained by the store have great limitations, and cannot truly reflect the consumption of users in the store, resulting in certain deviations in the analysis results based on such user data. And then interfere with the correct operation strategy of the store

For example, a restaurant can only obtain the data of some checkout users through checkout, but cannot obtain the user data of other users who do not need to checkout, so that the restaurant cannot obtain richer user data, which affects the restaurant's operational decision-making

[0004] It can be seen that in related technologies, the efficiency of obtaining user data by offline stores is low, which greatly limits the richness of user data and affects the mining of user data by stores.

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