Transforming Movie Recommendations with Advanced Machine Learning: A Study of NMF, SVD,and K-Means Clustering
This work addresses the need for better user experience in movie recommendation systems, but it appears incremental as it applies existing methods without novel breakthroughs.
The study tackled the problem of improving movie recommendations by developing a system using NMF, SVD, and K-Means clustering, resulting in high accuracy and relevance in personalized suggestions.
This study develops a robust movie recommendation system using various machine learning techniques, including Non- Negative Matrix Factorization (NMF), Truncated Singular Value Decomposition (SVD), and K-Means clustering. The primary objective is to enhance user experience by providing personalized movie recommendations. The research encompasses data preprocessing, model training, and evaluation, highlighting the efficacy of the employed methods. Results indicate that the proposed system achieves high accuracy and relevance in recommendations, making significant contributions to the field of recommendations systems.