Content publishing method and device, computer readable storage medium and electronic equipment

Abstract

The embodiment of the invention provides a content publishing method and device, a medium and electronic equipment, and relates to the technical field of data processing. The method comprises the following steps: acquiring initial recommendation content, and issuing the initial recommendation content by adopting an initial distribution mode; wherein the recommendation number of the initial recommendation content is smaller than or equal to a first threshold; monitoring the recommendation number of the initial recommendation content, determining first recommendation content according to the initial recommendation content, wherein the first recommendation content is the initial recommendation content with the recommendation number larger than a first threshold value; determining a first content evaluation parameter corresponding to the first recommendation content according to first user operation data corresponding to the first recommendation content; and determining second recommendation content from the first recommendation content according to the first content evaluation parameter, and publishing the second recommendation content according to the first content evaluation parameter. In the content recommendation process, low-quality content can be effectively filtered, and distribution of high-quality content is accelerated.

Description

technical field [0001] The embodiments of the present disclosure relate to the technical field of data processing, and more specifically, the embodiments of the present disclosure relate to a content distribution method, a content distribution device, a computer-readable storage medium, and an electronic device. Background technique [0002] This section is intended to provide a background or context for implementations of the disclosure that are recited in the claims. The descriptions herein are not admitted to be prior art by inclusion in this section. [0003] The recommendation system can predict the user's future behavior and interests based on the user's historical behavior and interests, so a large amount of user behavior data has become an important part and prerequisite of the recommendation system. For a newly developed website, how to design a personalized recommendation system without a large amount of user data and make users satisfied with the recommendation r...

AI Technical Summary

Problems solved by technology

However, in the process of using the information flow recommendation system of the prior art for recommendation, there is a problem that the low-quality content cannot be effectively filtered and the high-quality content is screened out, resulting in the quality of the recommended content cannot be guaranteed, and the user experience is reduced.

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