Movie Recommendation System Using Director-Based

Abstract

A recommendation system saves the user the time and effort of searching for information by analyzing their profile and recommending the most appropriate content. To perform recommendations, a variety of techniques have been proposed, including content-based, collaborative, and hybrid filtering. Recommendation systems are used to suggest content such as books, music, and movies. The film business, in particular, makes movie recommendations using collaborative filtering that is based on genres and is frequently utilized in film recommendation systems. When customers first come across movie suggestion services or have certain movie interests, such as preferences for directors, this method may not work as well. This inspired us to propose a director-based recommendation system that uses content-based filtering and takes into account the genres of 5,000 records of Kaggle movie data as well as information on the filmographies of the directors. The cosine similarity function is used to assess the effectiveness and performance of the recommended system, and the results are very satisfactory.