User portrait based personalized precision marketing method, server and storage medium

Abstract

The invention provides a user portrait based personalized precision marketing method, a server and a storage medium. The method comprises the steps of obtaining label information of a user, building auser portrait according to the label information, obtaining the current Internet surfing behavior of the user, performing prediction and analysis on the behavior, screening out contents which the user may like or be interested in based on the user portrait when the user is predicted and analyzed to have a purchase or consumption intention, and carrying out personalized recommendation according tothe contents which the user may like or be interested in. Each user has a unique user portrait, and a list of contents which the user may like or interested in can be screened out through performingdeep analysis by combining the user portrait and the current Internet surfing behavior, thereby being capable of well performing personalized recommendation according to the condition of each user, improving the recommendation accuracy, and also being capable of well solving a problem of cold start of new users at the same time.

Description

technical field [0001] The present invention relates to the field of computer processing technology, in particular to a personalized and precise marketing method based on user portraits, a server and a storage medium. Background technique [0002] With the development of the Internet, people's lives are more and more closely linked with the Internet. In this fast-paced era, users hope to quickly find the products they need through the Internet, but massive product data is continuously generated on the Internet every day, which makes it difficult for Internet users to quickly find the information they need or are interested in . In order to allow users to quickly find the products they are interested in, the traditional collaborative filtering technology recommends based on the user's historical purchases. However, for new users, they often face cold start problems, and it is difficult to get accurate recommendations. The user's interest may be one-time. If the recommenda...

AI Technical Summary

Problems solved by technology

In this fast-paced era, users hope to quickly find the products they need through the Internet, but massive product data is continuously generated on the Internet every day, which makes it difficult for Internet users to quickly find the information they need or are interested in

In order to allow users to quickly find the products they are interested in, the traditional collaborative filtering technology recommends based on the user's historical purchases. However, for new users, they often face cold start problems, and it is difficult to get accurate recommendations. The user's interest may be one-time. If the recommendation is only based on historical purchases, the accuracy of the recommendation is often not high

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