Enhancing communication and interaction in the movie industry based SparkMLlib's recommendation system - Chakouk, Said; Zitouni, Abdelkerim; Tchagafo, Nazif; Belaid, Ahiod (Institute of Advanced Engineering and Science, 2025)
In the ever-evolving landscape of streaming platforms, recommendation systems contribute significantly to enhancing the user experience. This article examines the significance of these systems in suggesting movies, analyzing their impact on user satisfaction and platform performance. Utilizing SparkMLlib, a powerful tool for large-scale data processing, we explore various recommendation techniques, including collaborative filtering and content-based filtering. We highlight the dimension of digital communication to further enhance the accuracy of recommendations and foster greater user engagement. Our study also addresses the challenges and future opportunities related to recommendation systems, emphasizing the need for transparency and ethical algorithms. This research highlights the potential for recommendation systems to revolutionize the digital entertainment landscape and shape the future of the movie industry.