Information pushing method and device based on instant messaging, terminal and storage medium

Abstract

The invention relates to an information pushing method and device based on instant messaging, a terminal and a storage medium. The method comprises the following steps: extracting a keyword of a chatmessage in a session region; acquiring push information matched with the keyword from a server; selecting a display template according to the push information; reconstructing the push information according to the display template, and displaying the push information in a corresponding preset template interface, wherein the preset template interface cannot shield the session region. Through the information pushing method and device based on instant messaging, the terminal and the storage medium disclosed by the invention, the information can be pushed in real time according to the chat messagewithout providing the keyword by a user, and the interested content of the user can be precisely pushed.

Description

technical field [0001] The present invention relates to the technical field of the Internet, in particular to an instant messaging-based information push method, device, terminal and storage medium. Background technique [0002] Intelligent recommendation has been applied in various fields of the Internet. Many companies will launch intelligent recommendation of product-related content in their respective product forms based on the idea of ​​machine learning. In the traditional content intelligent recommendation, recommendations are made based on the search keywords entered by the user or personal favorite products in e-commerce. For example, on the search page, the user first enters the search keywords in the search box, and then according to the The operation is cumbersome to push related information based on specific keywords, while some shopping stores make recommendations based on users’ purchase behaviors, which has a certain lag, resulting in the recommended content n...

AI Technical Summary

Problems solved by technology

In the traditional content intelligent recommendation, the recommendation is based on the search keywords entered by the user or personal favorite products in the e-commerce. For example, on the search page, the user first enters the search keywords in the search box, and then according to the user input The keywords push relevant information, and the operation is cumbersome, while some shopping stores recommend based on the user's purchase behavior, there is a certain lag, and the recommended content cannot accurately meet the user's needs

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