This project was created for academic purposes on the subject of 'Learning Analytics' at the Aristotle University of Thessaloniki.
Recommender systems have become ubiquitous in our lives. The need to build robust movie recommendation systems is extremely important given the huge demand for personalized content of modern consumers.
In this project, we attempt to understand the different kinds of recommendation systems and compare their performance on the MovieLens dataset. It includes two of the most commonly used filtering techniques for RS. These are Content–Based Filtering (CBF) and Collaborative Filtering (CF).