Design and Implementation of a Personalized News System Based on SSM Architecture

In today's information age, personalized recommendations have permeated every aspect of people's lives, becoming an indispensable service on major mainstream websites. By considering users' past interaction habits and preferences, personalized recommendation systems cater to the unique needs of individuals, presenting content that aligns with their interests. However, research on news recommendations, compared to fields such as ecommerce and music, remains relatively scarce and faces significant challenges, particularly in identifying news choices that may engage readers amidst information overload. This study designed and implemented a personalized hot news recommendation mechanism based on collaborative filtering technology. The system was developed using the SSM framework, incorporating software engineering principles with UML modeling. The front-end interface was built and deployed using the Bootstrap framework, while the back-end interface construction was supported by the Layui front-end framework. MySQL served as the storage system for news information. The core services of the personalized news push system include user account creation and login, personal information management, information search, news content evaluation, customized content push, and administrator-end information supervision. The customized push service, in particular, relies on collaborative filtering technology, tag-based recommendation strategies, and mechanisms for tracking current hot topics to achieve its goals.