Implementation of a Movie Recommendation System Using Collaborative Filtering and the K-Nearest Neighbors Technique

Abstract

The rapid growth of video services has greatly expanded the number of available films, making it increasingly difficult for users to identify titles that match their preferences. This study implements a web-based movie recommendation system using collaborative filtering and the K-nearest neighbors (KNN) technique to generate personalized recommendations from similarities among user preferences. An experimental quantitative design was adopted through requirements analysis, system design, implementation, testing, and evaluation. User ratings were used to calculate inter-user similarity, after which KNN selected the most similar users as the basis for recommendation generation. The system was evaluated using black-box testing and a five-point Likert-scale questionnaire covering system performance, information use, system use, and user satisfaction. All functional test scenarios were classified as valid, while the questionnaire variables received mean scores in the good category. The findings indicate that the system can provide relevant recommendations through an interface that users perceive as effective and easy to use. Collaborative filtering combined with KNN therefore offers a practical approach for helping users find films that correspond to their preferences while improving the service quality of a web-based recommendation platform.