Transforming Movie Recommendations with Advanced Machine Learning: A Study of NMF, SVD,and K-Means Clustering

TL;DR

This paper presents a movie recommendation system utilizing NMF, SVD, and K-Means clustering to improve personalization and recommendation accuracy.

Contribution

It introduces a robust recommendation framework combining multiple machine learning techniques for enhanced personalization.

Findings

High accuracy in recommendations

Effective user clustering

Significant improvement over baseline methods

Abstract

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.