A Movie Recommendation System Using Machine Learning-Based Collaborative Filtering

Movie platforms contain large catalogs, making it difficult for users to select films that match their interests. This paper proposes a movie recommendation system that uses machine-learning-based collaborative filtering only. The system learns from historical user–movie ratings rather than using content-based similarity, genre matching, or a hybrid content model. After cleaning the rating data, it builds a user–item interaction matrix and trains collaborative models such as K-nearest neighbors (KNN) and matrix factorization using singular value decomposition (SVD). The trained model estimates ratings for movies a user has not rated and ranks candidates to produce a top-N recommendation list. MovieLens is proposed as a public dataset for implementation and reproducible evaluation. The system can be evaluated using mean absolute error (MAE), root mean squared error (RMSE), Precision@K, and Recall@K. No numerical experimental results are claimed because the models have not been run on a dataset as part of preparing this document. The proposed design provides a clear ML-only workflow and can be extended through parameter tuning, cold-start strategies, and user-interface development.