Movie Recommendation System

Because of the advent of the Internet, now have access to an abundance of data across many disciplines. However, consumers frequently face situations where they have a plethora of options to consider and could use some guidance navigating those options. An effective method for closing this gap is the use of recommendation systems. There are many different methods being used to develop recommender systems, but they can be broken down into two broad categories: content-based and collaborative filtering. In order to address the limitations of traditional recommender systems, researchers are increasingly turning to hybrid approaches, wherein multiple recommendation methods are combined. In addition to these three tried-and-true methods, recommendation quality can also be enhanced by employing a context-based recommender system. Several methods exist for modelling contextual information in a recommendation system, including pre-and post-filtering, as well as contextual modelling. In this paper propose a hybrid system that combines the best features of both pre-and post-filtering contextualization techniques. In order to make better movie recommendations, the proposed method will make use of a database that is rich in context and contains information such as user data, item data, ratings, and contextual information. The proposed method as a whole is broken down into stages. To generate the first set of recommendations, it will be first applying a contextual pre-filtering approach to the entire database based on the most important contextual attribute for a user, thereby reducing the multi-dimensional data into a reduced dataset. The recommendations are then sent to a contextual post filter, where they undergo additional processing in the form of filtering and adjustment in light of the other two pertinent contextual attributes for that user.