An Advanced Movie Recommendation System Leveraging Alternating Least Squares and Apache Spark for Scalable Data Processing

Nowadays, recommendation systems have revolutionized how we discover interests, employing an information filtering approach to predict user preferences. Typical application areas encompass books, news, music, videos, and movies. This paper introduces a movie recommendation system designed using the Alternating Least Squares (ALS) algorithm, a widely adopted collaborative filtering technique implemented in the PySpark ML library. The system is specifically tailored to handle large-scale datasets, making it well-suited for real-world applications. The core functionality of the system involves processing a dataset containing user ratings for movies. By applying the ALS algorithm, latent factors are learned for both users and movies. These latent factors acquire the essential likings of users with the intrinsic characteristics of movies. Leveraging these latent factors, the system predicts ratings, with an accuracy of 72.91%, that users would assign to unrated movies, thereby generating personalized movie recommendations.