A recommender system, or a recommendation system, is an information filtering system that attempts to predict a particular ‘rating’ or ‘preference’ a user would have for an item. It is widely used in different internet/online businesses such as Amazon, Netflix, Spotify, and also features in a wide range of social media like Facebook, Twitter and YouTube. By using recommender systems, these companies are able to provide better or more suited products/services/content that are personalized to a user based on his/her consumer behaviors. For the purpose of studying the intricate workings of Recommender Systems in further detail and to observe their role in presenting a user with similar items to their own preferences, it was decided to design and develop a web application based around a Movie recommender system. The purpose of this application is to observe the outcome a recommender system has with providing users with similar content whilst also personally honing my own skills in machine learning and new technologies.
My Thesis looked at the various algorithms and methods that are used to recommend suitable content for users based on their tastes and previous encounters. The thesis compares Collaborative and Content based filtering methods and explains the advantages and disadvantages that come with each different method as well as describing how the application performs and the technologies that were required to set it up.