Personalized Information Generation: A Review

Abstract

There are countless options for choosing news headlines, and finding the right balance between conveying an important message and capturing the reader's attention is the key to successful publishing. However, it is unfair to present the same information about the same topic to all readers; because no matter what the preferences and interests of different readers are, there may be confusion as to why a particular topic is presented to them and a good match may not be found between interests and requested topic. In this article, we present a new approach that addresses these problems by combining user profiling to provide personalized headlines and automated and human review methods to determine what you use for a particular headline. Our system uses a powerful key function to assign unique keywords to users based on their reading history, which is then used to transform generation titles. Through an in - depth analysis, we demonstrate the effectiveness of the proposed framework in delivering personalized headlines that suit the needs of the target audience. Our platform has the ability to improve the performance of data requests and facilitate the creation of personalized content.