Machine learning application in movie recommender systems: A case study of movielens website

Abstract

Movie recommendation systems are an important part of modern online movie platforms, assisting users in findning relevant and personalized movie suggestions from large catalogs. Using the MovieLens-1M dataset, this thesis investigates the effectiveness of three recommendation algorithms: K-Nearest Neighbors(K-NN), Random Forest, and Matrix Factorization. The study begins with an in-depth examnination of the Movielens-1M dataset, which contains a million ratings from a wide of users for thousands of films. To handle missing data, normalize ratings, and transform categorical features, various preprocessing techniques are used. The K-NN algorithm is implemented, utilizing cosine similarity as a distance metric to recommend movies based on user-item ratings. To optimize the algorithm’s performance, the impact of different parameter values for K and other hyperparameters is evaluated. The popular ensemble learning method Random Forest is then used to generate recommendations by constructing decision trees based on user and movie attributes. Matrix Factorization is used in addition to user-item ratings to uncover latent features and capture complex relationships between users and movies. The latent factors that result are used to predict personalized movie recommendations. To assess the performance of the implemented recommendation algorithms, evaluation metrics such as precision, recall used. Comparative analyses are performed to identify each algorithm’s strengths and weaknesses and to provide insights into their effectiveness in recommending movies to different users. Overall, by offering a thorough analysis of the K-NN, Random Forest, and Matrix Factorization algorithms, this thesis makes a contribution to the field of movie recommendation systems. The research results provide insightful information for creating and improving movie recommendation algorithms, enabling better user interactions and more active user participation on movie platforms.