Movie Recommendation System Using KNN, Cosine Similarity and Collaborative Filtering

Abstract

The movie recommendation system is becoming increasingly popular in the digital era. With the continuous emergence of a vast amount of movie resources, users are facing more and more choices. Therefore, an intelligent movie recommendation system can assist users in quickly finding movies that match their personal preferences, thereby enhancing user satisfaction and movie-watching experience . Our movie recommendation system recommends high-rated and well-reviewed films and TV shows to the general audience based on Netflix viewers’ ratings for them. These are the movies and shows that are considered worth watching. We utilize the Netflix open dataset, which consists of a large number of user ratings and movie information [1]. By merging the movie and rating datasets and performing data cleansing and preprocessing, we obtain a dataset suitable for models. Subsequently, we represent the movie rating data using sparse matrices and train a k-nearest neighbours (k-NN) algorithm to achieve personalized movie recommendations [2].Our approach considers both user rating preferences and movie similarities, enabling accurate and personalized recommendations for users. Through evaluation experiments and user surveys, we validate the effectiveness of our recommendation system in terms of accuracy and user satisfaction [3]. This research is significant for improving the effectiveness of movie recommendation systems and enhancing user experiences.