News Recommendation System Using Deep Learning Method

Abstract

The impressive amount of news information online, which is reflected in digital age, is a great problem affecting the provision of useful news to the readers effectively. The urgency of having intelligent systems that are able to filter and suggest news depending on the contents and not on the actions of the user is more eminent than ever. The study is dedicated to the development and deployment of a news recommendation system based on the Natural Language Processing (NLP) techniques to increase personalization and contextual relevance of news presentation. Raw textual information (news headlines descriptions) goes through the system, undergoes further sophisticated text preprocessing procedures and gets transformed to structured numeric formats ready to be used in the machine learning area. With deep learning structures, the extraction of semantic characteristics in images allows predicting the right categories, and this is how news is properly recommended regardless of the need to log in or track users. Personalized recommendations based on content-based analysis and sound feature extraction are provided in a manner that the privacy of the user is not compromised. This paper assists in the formation of ethical and effective news recommendation systems, and it proves how NLP and deep learning can be combined to manage the real-world information overloading. The proposed system is not only scalable, but also translates to other languages and other fields; the system has prospects of future development using user interaction and sentiment analysis capabilities.