Matrix Factorization for Movie Recommendations using Collaborative Filtering

This project uses matrix factorization techniques such as Alternating Least Squares (ALS) to perform collaborative filtering for movie recommendations. The system predicts missing ratings and recommends movies by finding patterns in user-item interactions. Matrix factorization improves the quality of recommendations, especially for sparse datasets.