Personalized recommendation method and apparatus

Abstract

The present invention discloses a personalized recommendation method and apparatus. The method comprises: capturing first context information corresponding to user data, and obtaining first recommendation content matched with the first context information; acquiring response information of a user for the first recommendation content, wherein the response information is used for representing whether the user is interested in the first recommendation content; according to the response information, determining whether to adjust the first recommendation content, and if yes, determining second recommendation content matched with the response information, wherein the first recommendation content and the second recommendation content are obtained by performing calculation according to a same recommendation algorithm; and adjusting the first recommendation content matched with the first context information into the second recommendation content. The method and apparatus disclosed by the present invention facilitate simplification of a personalized recommendation process and improvement of pertinence and accuracy of the recommendation process.

Description

technical field [0001] The present invention relates to the field of data processing, in particular to a personalized recommendation method and device. Background technique [0002] With the continuous development of information technology, in order to better serve users and improve user experience, personalized recommendation technology has emerged to provide users with personalized recommendation content that meets their needs. Usually, personalized recommendation technology is based on user data such as user interests and behaviors, combined with certain data analysis methods, to generate personalized recommendation content for users. [0003] When making personalized recommendations, user data can be combined with the recommendation algorithm to calculate the user's ratings for potential items, and potential items with higher ratings can be used as recommended content and provided to users. Among them, higher ratings can be understood as more than default value. That i...

AI Technical Summary

Problems solved by technology

[0005] When making personalized recommendations based on context information, a recommendation algorithm can be used to determine the recommended content for the user. If the user is not interested in the recommended content, a new recommendation algorithm can be used to determine the recommended content for the user again, and so on. , by constantly changing the recommendation algorithm, trying to recommend the content that the user is interested in, resulting in a complex personalized recommendation process with a large amount of calculation and low efficiency

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