User-driven Relevant News Update System (U.R.A.N.U.S)

Abstract

In an era characterized by information overload, the need for effective news recommendation systems has become paramount. This review paper provides a comprehensive examination of the methodologies, challenges, and advancements in the domain of news recommendation systems. It explores two primary technologies commonly employed in recommender systems: information filtering and collaborative filtering, elucidating their respective features, advantages, and limitations. The paper delves into the application of these technologies in personalized news reading applications, such as PIN, WebClipping2, and Group Lens, highlighting their unique approaches to user modeling and recommendation generation. Additionally, it discusses the integration of hybrid methods, which combine information filtering and collaborative filtering techniques, to enhance recommendation quality. The review also addresses key issues in user modeling, including the dynamic nature of user interests and the incorporation of temporal aspects in profile construction. Ethical considerations, such as fairness, diversity, and privacy, are examined in the context of news recommendation system design. Through a thorough analysis of performance metrics and comparative studies against baseline models, the paper evaluates the efficacy of various news recommendation approaches. Finally, it identifies future research directions and potential areas for innovation in the field, paving the way for the development of more sophisticated and user- centric news recommendation systems.