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.

翻译：本研究利用多种机器学习技术，包括非负矩阵分解（NMF）、截断奇异值分解（SVD）和K-Means聚类，开发了一个鲁棒的电影推荐系统。其主要目标是通过提供个性化的电影推荐来提升用户体验。研究涵盖了数据预处理、模型训练与评估，重点突出了所采用方法的有效性。结果表明，所提出的系统在推荐中实现了较高的准确性和相关性，为推荐系统领域做出了重要贡献。