A Content-Based Recommender System for News Articles

Recommendation systems are a type of artificial intelligence that are used to predict what a user may want to watch, read, or purchase next. They are designed to personalize the user experience by suggesting items that are relevant and appealing to the individual, and are often found on websites and apps. There are several types of recommendation systems, including collaborative filtering, content-based filtering, and hybrid systems, which use different approaches to make recommendations. These systems have become an important tool for businesses and organizations in a variety of industries, and have greatly impacted the way that users interact with the internet. This article provides an overview of recommendation systems, including their types, applications, and challenges. It also discusses the potential benefits and limitations of these systems. We also explain a content-based recommendation system that helps readers to the relevant articles to them. In addition, we have implemented a content-based recommendation system for news media that helps readers find their relevant articles fast. The recommendation system is implemented in Python and used a publicly available Data Set to build and train the model. Keywords: Recommender system, content-based, recommendation, Collaborative Filtering, Hybrid Recommendation.