Content recommendation method and system, computer equipment and storage medium

Abstract

The invention provides a content recommendation method and system, computer equipment and a readable storage medium. The method comprises the steps of creating a recommendation queue for each user; according to the behavior information of the content browsed by the user, establishing an association relationship between the user and a publisher recommending the content; when the publisher recommends the content, writing the recommended content into a recommendation queue of the user associated with the publisher; and recommending the content in the recommendation queue of the user to the user.According to the technical scheme, one recommendation queue can be maintained for each user, the list content is updated in real time based on the behaviors of the users, the recommendation data is directly taken out from the list every time the users obtain the recommendation data, the recommendation content is adjusted in real time based on the behaviors of the users while large-scale computingresource consumption is avoided, and the recommendation efficiency is guaranteed.

Description

technical field [0001] The present disclosure belongs to the technical field of the Internet, and specifically relates to a content recommendation method, a content recommendation system, a computer device, and a computer-readable storage medium. Background technique [0002] With the rapid development of the mobile Internet era, the information that people come into contact with is also growing explosively. There are more and more content providers in various content service platforms, and thousands of users upload videos, news, articles and other information every day, and how to recommend so much content to users and provide users with interesting content , is the problem that the content service platform is currently facing. [0003] Most of the current mainstream recommendation systems involve a series of processing modules such as user portrait construction, behavior data collection, content recall, recommendation model calculation, and ranking; each recommendation ne...

AI Technical Summary

Problems solved by technology

[0003] Most of the current mainstream recommendation systems involve a series of processing modules such as user portrait construction, behavior data collection, content recall, recommendation model calculation, and ranking; each recommendation needs to extract the final recommendation result from the content library, and with the content As the database becomes larger and larger, the computing and storage resources required by the recommendation algorithm will grow in a curve, requiring a large amount of capital, software and hardware resources to ensure the computing efficiency of the recommendation. The cost is high and the efficiency is low, and this is for small-scale platforms or clusters. saying it will become difficult to achieve

Images