Model training method, model-based red envelope material laying prediction method and device

Abstract

The invention discloses a model training method, a model-based red envelope material laying prediction method and a device thereof. The model training method comprises the following steps: acquiring at least one scanning code receiving red envelope data and at least one scanning code payment data; according to the related information of at least one setting merchant, each piece of acquired data being respectively extracted with features to obtain a training sample corresponding to the setting merchant, wherein the training sample takes the extracted eigenvalue as an input value; a prediction model being obtained by training the obtained training samples with an unsupervised learning algorithm, wherein the prediction model takes the eigenvalues of the code scanning red packet data and the eigenvalues of the code scanning payment data as input values and the probability of laying the red packet materials by a business as output values.

Description

technical field [0001] The embodiments of this specification relate to the technical field of data processing, and in particular to a model training method, a model-based red envelope material laying prediction method and device. Background technique [0002] With the development of Internet technology and the popularization of smart terminals, the competition among various payment applications has become increasingly fierce. In order to compete for the share of "offline payment", the marketing team of some payment applications proposed " The marketing strategy of "scan code to receive red envelopes" is to lay out promotional materials (hereinafter referred to as red envelope materials) printed with red envelope QR codes in cooperative stores, such as roll-up banners, brochures, etc. After users arrive at the store, they can Scan the QR code of the red envelope through the app installed on the mobile phone to get a red envelope with a certain amount. When you make an offline...

AI Technical Summary

Problems solved by technology

However, if the former method is used, it will consume a lot of manpower and material costs; if the latter method is used, the accuracy of the supervision results cannot be guaranteed due to the limited number of photos and the authenticity of the photos.

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