Content recommendation method and device based on artificial intelligence and computer equipment

Abstract

The invention relates to a content recommendation method and device based on artificial intelligence and computer equipment. The method comprises the following steps: acquiring source domain user characteristics corresponding to a new user of a target domain in a source domain; performing feature mapping on the source domain user features through a trained task-oriented mapping model to obtain mapped user features mapped from the source domain to the target domain; through a pre-trained content recommendation model, carrying out click prediction of the recommended content according to the mapping user characteristics and the target domain content characteristics of each candidate content in the target domain; screening to-be-recommended contents from the candidate contents according to a click prediction result; pushing the to-be-recommended content to the new user of the target domain. By adopting the method, the content recommendation accuracy of the new user can be effectively improved.

Description

technical field [0001] The present application relates to the technical field of the Internet, in particular to an artificial intelligence-based content recommendation method, device, computer equipment and storage medium. Background technique [0002] With the rapid development of Internet technology, recommendation technology based on big data and artificial intelligence has emerged as the times require, and many recommendation systems have emerged, which can intelligently recommend content that users are interested in to users. The recommendation system can usually predict the user's preference by analyzing the user's historical behavior information, so as to recommend the information that the user is interested in, so as to meet the user's personalized recommendation needs. [0003] In a recommendation system, for new users who do not have historical data in this field, a common practice is to recommend information with higher popularity to new users according to the pop...

AI Technical Summary

Problems solved by technology

However, in the traditional way, due to the lack of user behavior characteristics of new users, it is difficult to predict the interests of users themselves, and it is impossible to accurately recommend personalized content to new users, resulting in low accuracy of information recommendation

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