News Recommendation System Design and Implementation Using Collaborative Filtering

This paper presents the design and implementation of a news recommendation system based on the collaborative filtering algorithm. The system aims to recommend relevant news articles to users based on their past interactions and preferences. Collaborative filtering is a widely used technique in recommender systems that leverages user-item interactions to predict user preferences for unseen items. The system employs a user-based collaborative filtering approach, where the similarity between users is calculated based on their shared interests in news articles. The system then recommends news articles to a user based on the ratings or preferences of similar users. The system has been implemented using a combination of Python and machine learning libraries, including scikit-learn and pandas. The system has been evaluated on a real-world dataset of news articles and user interactions, demonstrating its effectiveness in recommending relevant news articles to users.