[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290430-105":59,"doc-detail-290430-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","favorite-book-prediction-system-using-machine-learning-algorithms-research-paper","FAVORITE BOOK PREDICTION SYSTEM USING MACHINE LEARNING ALGORITHMS - Research Paper","","Recent years have seen rapid deployment of Artificial Intelligence (AI), enabling systems to make intelligent decisions and potentially transforming modern library operations. Introducing AI in libraries remains challenging, and this research proposes smart-library improvements to enhance user service quality via machine learning (ML). The study evaluates Random Forest and multiple boosting methods including LGBM, HGB, XGB, CatBoost, AdaBoost, and Gradient Boosting for identifying and classifying favorite books. Experiments on Art Garfunkel's Library show LGBM reaches 94.9367% accuracy.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/favorite-book-prediction-system-using-machine-learning-algorithms-research-paper/290430/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/favorite-book-prediction-system-using-machine-learning-algorithms-research-paper/290430.png","ImageObject",300,407,{"name":92,"@type":93},"Ivy","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Which machine learning algorithms are evaluated for favorite book identification?","Question",{"text":113,"@type":114},"The study evaluates Random Forest and boosting algorithms including LGBM, HGB, XGB, CatBoost, AdaBoost, and Gradient Boosting.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What dataset is used in the experiments?",{"text":118,"@type":114},"The experiments use the publicly available dataset Art Garfunkel's Library.",{"name":120,"@type":111,"acceptedAnswer":121},"What performance does the proposed model achieve, and which algorithm is best?",{"text":122,"@type":114},"Results show LGBM achieves outstanding performance with 94.9367% accuracy, outperforming Random Forest and other boosting ML algorithms.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290430,1789641673,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","FAVORITE BOOK PREDICTION SYSTEM USING MACHINE LEARNING ALGORITHMS  \nDersin Daimari1, Subhash Mondal2*, Bihung Brahma3, Amitava Nag4  \nDept. of HSS, Central Institute of Technology Kokrajhar 1,3  \nDept. of CSE, Central Institute of Technology Kokrajhar2,4  \n[dersindaimari@gmail.com](dersindaimari@gmail.com1)[1](dersindaimari@gmail.com1), [ph22cse1001@cit.ac.in](ph22cse1001@cit.ac.in2)[2](ph22cse1001@cit.ac.in2)*, [b.brahma@cit.ac.in](b.brahma@cit.ac.in3)[3](b.brahma@cit.ac.in3),  \n[amitava.nag@cit.ac.in](amitava.nag@cit.ac.in4)[4](amitava.nag@cit.ac.in4)  \nReceived : 05 April 2023, Revised: 08 May 2023, Accepted : 08 May 2023  \n*Corresponding Author  \nABSTRACT  \nRecent years have seen the rapid deployment of Artificial Intelligence (AI) which allows systems to take intelligent decisions. AI breakthroughs could radically change modern libraries' operations. However, introducing AI in modern libraries is a challenging task. This research explores the potential for smart libraries to improve the caliber of user services through the use of machine learning (ML) techniques. The proposed work investigates machine learning methods such as Random Forest (RF) and boosting algorithms, including Light Gradient Boosting Machine (LGBM), Histogram-based gradient boosting (HGB), Extreme gradient boosting (XGB), CatBoost (CB), AdaBoost (AB), and Gradient Boosting (GB) for the task of identifying and classifying Favorite books and compares their performances. Comprehensive experiments performed on the publicly available dataset (Art Garfunkel's Library) show that the proposed model can effectively handle the task of identifying and classifying Favorite books. Experimental results show that LGBM has achieved outstanding performance with an accuracy rate of 94.9367% than Random Forest and other boosting ML algorithms. This empirical research work takes advantage of AI adoption in libraries using machine learning techniques. To the best of our knowledge, we are the first to develop an intelligent application for the modern library to automatically identify and classify Favorite books. Keywords: Machine Learning, artificial intelligence (AI), Smart Library, Light Gradient Boosting Machine (LGBM), Boosting Model  \n1. Introduction  \nLibraries have long been one of the most important places to find knowledge. These libraries, formerly only considered information repositories, have a new perspective in the current Information and communication technology (ICT) era. Whether traditional or \"smart,\" libraries aim to share real-time information with their users or members. Information and communication technology (ICT) advancements like artificial intelligence (AI)(Iskandar et al., 2022), machine learning (ML), the Internet of Things (IoT) (Irawan et al., 2022), and fuzzy logic (Perdana et al., 2022) have recently outpaced traditional science and technology in a remarkable way. These new ICTs have the capacity to use analytics and machine learning for trend analysis and prediction, and they can allow modernizations that raise the caliber of services provided by contemporary libraries. This makes it possible for end users to be satisfied. While a computer program designed to mimic human intelligence is the standard definition of artificial intelligence (AI), the truth is much more nuanced. The Google search engine and Chat-GPT are probably the most notable examples of how daily applications of AI are already fully incorporated into society through technology like autonomous vehicles, robotics, intelligent chatbots, and so on.  \nMachine Learning is a subfield of artificial intelligence that enables machines to make intelligent decisions without human interference through sophisticated mathematical and statistical methods with the help of devices or human-generated data (Garg & Mago, 2021)(Robles Mendo et al. , 2021) (Pasa Uysal, 2022) . This idea of automating complex tasks has generated high interest in different applications such as smart healthcare, smart","cbCaifBRTWXjHzHk","https://ap.wps.com/l/cbCaifBRTWXjHzHk","pdf",790402,"English","# Abstract\n# 1. Introduction\n# 2. Literature Review\n# 3. Proposed Method\n# 4. Experimental Results\n# 5. Conclusion","[{\"question\":\"Which machine learning algorithms are evaluated for favorite book identification?\",\"answer\":\"The study evaluates Random Forest and boosting algorithms including LGBM, HGB, XGB, CatBoost, AdaBoost, and Gradient Boosting.\"},{\"question\":\"What dataset is used in the experiments?\",\"answer\":\"The experiments use the publicly available dataset Art Garfunkel's Library.\"},{\"question\":\"What performance does the proposed model achieve, and which algorithm is best?\",\"answer\":\"Results show LGBM achieves outstanding performance with 94.9367% accuracy, outperforming Random Forest and other boosting ML algorithms.\"}]","FAVORITE BOOK PREDICTION SYSTEM USING MACHINE LEARNING ALGORITHMS - Research Paper | PDF",23]