Movie Recommendation Using Collaborative Filtering Method and K-Nearest Neighbours: A Case Study on Netflix

Streaming platforms like Netflix have become very popular in today's digital age, but users often need help finding movies or TV shows that suit their preferences. The Collaborative Filtering method has been used in recommendation systems to look for patterns of similarity among users with similar preferences. However, this method has limitations in situations with little user data or quick changes in preferences. To overcome this problem, this study proposes using the Collaborative Filtering method with the K-Nearest Neighbors (K-NN) algorithm. This research aims to develop a better recommendation system on Netflix by providing more personalized and accurate recommendations. The K-NN method uses user similarities based on genre preferences to provide relevant recommendations. Hopefully, this recommendation system can enhance the user experience by providing appropriate recommendations even in situations with little user data or quick changes in preferences. By implementing the Collaborative Filtering method with the K-NN algorithm, this research hopes to provide more relevant and interesting recommendations, helping users find content that matches their interests more efficiently.