Rule-Based Personalized News Recommendation Web Application with Fake News Detection

A rule-based personalised news recommendation web app that also checks stories for signs of being fake.

A rule-based personalised news recommendation web app that also checks stories for signs of being fake. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners building full-stack skills: user accounts and roles, a database-backed back end, a responsive front end and deployment.

The main parts are personalised recommendations ranked by relevance, secure sign-up with role-based access, a database-backed back end with a clear API, a responsive interface for each type of user, and an admin view to manage records. The reference build uses scikit-learn, React, Node.js, Express, and PostgreSQL, with a clear project structure, setup instructions and sample data so that it can be run and extended.