[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126786-en":3,"doc-seo-126786-105":30,"detail-sidebar-cat-0-en-105":91},{"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},126786,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A SMART HYBRID ENHANCED RECOMMENDATION AND PERSONALIZATION ALGORITHM USING MACHINE LEARNING - Project Presented for Master of Science in Computer Science - May 2024","Streaming services make recommendation accuracy essential for user satisfaction. This project proposes the Smart Hybrid Enhanced Recommendation and Personalization Algorithm (SHERPA), a machine learning approach for movie suggestions. SHERPA integrates TF-IDF content-based filtering with Alternating Least Squares (ALS) plus Weighted Regularization to improve tailored ranking quality. Evaluation uses over 50 million Netflix ratings from roughly 480,000 users spanning 17,000 titles, comparing SHERPA against traditional hybrid models via RMSE during training, testing, and validation.","A SMART HYBRID ENHANCED  \nRECOMMENDATION AND PERSONALIZATION  \nALGORITHM USING MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in Computer Science  \nby  \nAswin Kumar Nalluri  \nA SMART HYBRID ENHANCED  \nRECOMMENDATION AND PERSONALIZATION  \nALGORITHM USING MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nAswin Kumar Nalluri  \nMay 2024  \nApproved by:  \nDr. Yan Zhang, Advisor, Computer Science and Engineering  \nDr. Yunfei Hou , Committee Member  \nDr. Jennifer Jin , Committee Member  \n© 2024 Aswin Kumar Nalluri  \nABSTRACT  \nIn today’s age of streaming services, the effectiveness and precision of recommendation systems are crucial in improving user satisfaction. This project introduces the Smart Hybrid Enhanced Recommendation and Personalization Algorithm (SHERPA) a cutting-edge machine learning approach aimed at transforming how movie suggestions are made. By combining Term Frequency Inverse Document Frequency (TF-IDF) for content based filtering and Alternating Squares (ALS) with Weighted Regularization for filtering SHERPA offers a sophisticated method for delivering tailored recommendations.  \nThe algorithm underwent evaluation using a dataset that included over 50 million ratings from 480,000 Netflix users encompassing 17,000 movie titles. The performance of SHERPA was meticulously compared to traditional hybrid models demonstrating a 70% enhancement in prediction accuracy based on Root Mean Square Error (RMSE) metrics during training, testing and validation phases.  \nThese findings highlight SHERPAs capability to understand and cater to users’ subtle preferences representing an advancement in personalized recommendation systems.  \nACKNOWLEDGEMENTS  \nI owe a heartfelt thanks to my advisor Dr. Yan Zhang for her wisdom, continuous support, and encouragement for my project \"Smart Hybrid Enhanced Recommendation and Personalization Algorithm (SHERPA)\" .  \nI am sincerely thankful to Dr. Yunfei Hou and Dr. Jennifer Jin for agreeing to be part of my committee and for believing in me for the successful completion of my project. Your insights have helped me, and your willingness to share your expertise has deeply enriched my work.  \nI am also thankful to High Performance Computing Program team at California State University San Bernardino for utilizing the HPC resources to finalize my results.  \nA special note of gratitude to my family and friends, whose encouragement has been my source of strength and motivation throughout the project.  \nTABLE OF CONTENTS  \nABSTRACT .......................................................................................................... iii  \nACKNOWLEDGEMENTS .....................................................................................iv  \nLIST OF TABLES ............................................................................................... viii  \nLIST OF FIGURES ............................................................................................... ix  \nCHAPTER ONE: INTRODUCTON ....................................................................... 1  \nBackground................................................................................................ 1  \nSignificance ............................................................................................... 1  \nPurpose ..................................................................................................... 2  \nCHAPTER TWO: LITERATURE SURVEY ........................................................... 3  \nTraditional Machine Learning Approaches................................................. 3  \nModern Machine Learning Approaches ..................................................... 3  \nCHAPTER THREE: DATA PREPARATION ......................................................... 5  \nData Collection ....................","cbCaiccWccA8Kvfn","https://ap.wps.com/l/cbCaiccWccA8Kvfn","pdf",995334,1,59,"English","en",105,"# ABSTRACT\n# ACKNOWLEDGEMENTS\n# LIST OF TABLES\n# LIST OF FIGURES\n# CHAPTER ONE: INTRODUCTON\n## Background\n## Significance\n## Purpose\n# CHAPTER TWO: LITERATURE SURVEY\n## Traditional Machine Learning Approaches\n## Modern Machine Learning Approaches\n# CHAPTER THREE: DATA PREPARATION\n## Data Collection\n## Movie Titles Dataset File Description\n## Movie Ratings Dataset File Description\n## Data Cleaning\n## Data Pre-processing\n## Data Parsing\n## Data Structuring\n## Format Handling Issues\n# CHAPTER FOUR: METHODOLOGY\n## Term Frequency-Inverse Document Frequency\n## Singular Value Decomposition\n## Mathematical Formulation of SVD\n## The Mechanics of SVD\n## SVD to Make Predictions\n## Alternating Least Squares\n## Update Procedure\n## Alternating Least Squares with Weighted Regularization\n## The Loss Function\n## Example Application of ALS\n## Content Based Filtering\n## Collaborative","[{\"question\":\"What is SHERPA and what problem does it address?\",\"answer\":\"SHERPA is a smart hybrid algorithm designed to improve movie recommendation quality in streaming services by learning users’ preferences more precisely.\"},{\"question\":\"How does SHERPA combine content-based and collaborative techniques?\",\"answer\":\"SHERPA uses TF-IDF for content-based filtering and Alternating Least Squares (ALS) for collaborative filtering, enhanced with Weighted Regularization.\"},{\"question\":\"What dataset and evaluation metrics were used to test SHERPA?\",\"answer\":\"The evaluation uses a dataset with over 50 million Netflix ratings from 480,000 users and 17,000 movie titles. Performance is compared using Root Mean Square Error (RMSE) across training, testing, and validation phases.\"}]","A SMART HYBRID ENHANCED RECOMMENDATION AND PERSONALIZATION ALGORITHM USING MACHINE LEARNING - Project Presented for Master of Science in Computer Science - May 2024 | PDF",1785934779,149,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-smart-hybrid-enhanced-recommendation-and-personalization-algorithm-using-machine-learning-project-presented-for-master-of-science-in-computer-science-may-2024","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-smart-hybrid-enhanced-recommendation-and-personalization-algorithm-using-machine-learning-project-presented-for-master-of-science-in-computer-science-may-2024/126786/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is SHERPA and what problem does it address?","Question",{"text":75,"@type":76},"SHERPA is a smart hybrid algorithm designed to improve movie recommendation quality in streaming services by learning users’ preferences more precisely.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does SHERPA combine content-based and collaborative techniques?",{"text":80,"@type":76},"SHERPA uses TF-IDF for content-based filtering and Alternating Least Squares (ALS) for collaborative filtering, enhanced with Weighted Regularization.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset and evaluation metrics were used to test SHERPA?",{"text":84,"@type":76},"The evaluation uses a dataset with over 50 million Netflix ratings from 480,000 users and 17,000 movie titles. Performance is compared using Root Mean Square Error (RMSE) across training, testing, and validation phases.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]