[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124020-en":3,"doc-seo-124020-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124020,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Transforming Movie Recommendations with Advanced Machine Learning - A Study of NMF, SVD, and K-Means Clustering","A robust movie recommendation system is developed using advanced machine learning methods, including NonNegative Matrix Factorization (NMF), Truncated Singular Value Decomposition (SVD), and K-Means clustering. The work aims to improve user experience through personalized recommendations by covering data preprocessing, model training, and systematic evaluation. Grounded in collaborative filtering and clustering principles, the approach uses sparse matrix factorization and user segmentation to capture latent preferences. Results show the proposed system delivers high accuracy and strong recommendation relevance, supporting meaningful contributions to recommendation system research.","Transforming Movie Recommendations with Advanced Machine Learning: A Study of NMF, SVD,  \nand K-Means Clustering  \n1st, * Yubing Yan  \nIndependent Researcher Los Angeles, USA  \n* [Corresponding author: yanyubing@mail.com](Corresponding author: yanyubing@mail.com)  \n3rd Zhuoyue Wang  \nUniversity of California, Berkeley New York, USA [zhuoyue_wang@berkeley.edu](zhuoyue_wang@berkeley.edu)  \n5th Chengqian Fu  \nIndependent Researcher Baltimore, USA[cqfu728@gmail.com](cqfu728@gmail.com)  \n2nd Camille Moreau  \nIndependent Researcher Paris, France  \n[camille_scholar@protonmail.com](camille_scholar@protonmail.com)  \n4th Wenhan Fan  \nIndependent Researcher New York, USA [finncontactplus@gmail.com](finncontactplus@gmail.com)  \nThis study develops a robust movie recommendation system using various machine learning techniques, including NonNegative 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 recommendation systems.  \nKeywords-recommendation system; machine learning; NonNegative Matrix Factorization; Truncated Singular Value Decomposition; K-Means clustering  \nI. INTRODUCTION  \nThe proliferation of digital content has necessitated the development of effective recommendation systems to aid users in navigating vast amounts of data. Movie recommendation systems, in particular, have gained prominence due to the sheer volume of content available on streaming platforms. This research aims to explore and implement advanced machine learning techniques [1-6] to create a high-performing movie recommendation system. The study addresses the following research questions: What are the most effective machine learning models [7-12] for movie recommendations? How do these models compare in terms of accuracy and relevance? What improvements can be made to existing systems to enhance user satisfaction? In particular, Zhao et al. (2024) [1] provide a robust framework for optimizing recommendation systems using Multi-Agent Reinforcement Learning (MARL) . Their work demonstrates significant improvements in key performance metrics, such as click-through rate (CTR) and conversion rate, which have inspired the methodology  \nemployed in this study to enhance recommendation accuracy and user satisfaction.  \nII. THEORETICAL FRAMEWORK  \nThis research is grounded in the principles of collaborative filtering and clustering algorithms. Collaborative filtering, a widely used technique in recommendation systems, leverages user-item interactions to predict user preferences. This study employs both memory-based and model-based collaborative filtering approaches. Additionally, clustering algorithms, particularly K-Means, are utilized to segment users into distinct groups based on their viewing patterns. Drawing inspiration from Zhao et al. (2024) [1], this study incorporates a cooperative multi-agent model that aligns with the foundational principles of collaborative filtering. By utilizing the MultiAgent Recurrent Deterministic Policy Gradient (MA-RDPG) algorithm, as suggested by Zhao et al., this research aims to optimize overall system performance through enhanced cooperative strategies and effective clustering techniques.  \nIII. LITERATURE REVIEW  \nPrevious studies have extensively explored collaborative filtering techniques for recommendation systems. Sarwar et al.(2001) [13] demonstrated the effectiveness of matrix factorization in uncovering latent user-item interactions. Korenet al. (2009) [14] further refined these techniques, leading to significant improvements in recommendation accuracy. Zhao et al. (2024) also explore content-based filtering ","cbCaimc81boJ0vRl","https://ap.wps.com/l/cbCaimc81boJ0vRl","pdf",535133,1,4,"English","en",105,"# Introduction\n# Theoretical Framework\n# Literature Review\n# Methodology\n## Data Preprocessing","[{\"question\":\"Which machine learning techniques are used to build the movie recommendation system?\",\"answer\":\"The study employs NonNegative Matrix Factorization (NMF), Truncated Singular Value Decomposition (SVD), and K-Means clustering to model preferences and segment users.\"},{\"question\":\"How is the dataset prepared before training the models?\",\"answer\":\"Data preprocessing handles missing values via mean/mode imputation, normalizes ratings to a common range, and converts the rating data into a sparse matrix format.\"},{\"question\":\"How is model performance evaluated in the research?\",\"answer\":\"The models are trained using a train-test split and assessed with metrics including Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE).\"}]","Transforming Movie Recommendations with Advanced Machine Learning - 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