[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-259915-105":53,"doc-detail-259915-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","jotpars-a-job-title-prediction-and-recommendation-system-for-it-professionals","JoTPaRS - A Job Title Prediction and Recommendation System for IT Professionals","","In today’s fast-evolving IT landscape, job seekers and recruiters struggle to align candidate qualifications with dynamic job roles and salary expectations. Traditional recruitment approaches often rely on manual review or keyword-based algorithms, lacking contextual understanding and producing suboptimal recommendations. JoTPaRS proposes a job title prediction and recommendation system using machine learning and natural language processing, supporting multi-label classification, salary range suggestions, and ranking enhanced by a Game Theory-based optimization layer for risk-sensitive decision making.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/jotpars-a-job-title-prediction-and-recommendation-system-for-it-professionals/259915/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/jotpars-a-job-title-prediction-and-recommendation-system-for-it-professionals/259915.png","ImageObject",442,249,{"name":88,"@type":89},"McQueen","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-11-04","2026-09-13",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",13,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"What problem does JoTPaRS address in IT hiring?","Question",{"text":109,"@type":110},"JoTPaRS addresses the difficulty of matching candidate qualifications to dynamic IT job roles, where conventional keyword-based or manual screening often fails to capture context and leads to poor shortlisting.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"How does JoTPaRS predict and recommend job titles?",{"text":114,"@type":110},"JoTPaRS uses machine learning and natural language processing to predict relevant IT job titles from user-provided descriptions, skills, and experience levels, including multi-label classification for recommendations.",{"name":116,"@type":107,"acceptedAnswer":117},"What is the role of the Game Theory-based optimization layer?",{"text":118,"@type":110},"The Game Theory-based layer selects the most suitable job title among multiple predictions by modeling the decision as a risk-sensitive payoff game, improving the ranking of recommendations and user satisfaction.","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},259915,1791729943,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":136,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":141,"read_time":142,"preview_page_count":4},5909890329169,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","JoTPaRS: A Job Title Prediction and Recommendation System for IT  \nProfessionals  \nShafika Rahman [shafikarahman193120004@student.ndub.edu.bd](shafikarahman193120004@student.ndub.edu.bd)  \nDepartment ofCSE,  \nNotre Dame University Bangladesh, Motijheel, 1214, Dhaka, Bangladesh.  \nFernaz Narin Nur [fernaznur@gmail.com](fernaznur@gmail.com)  \nDepartment ofCSE,  \nDaffodil International University, Savar, 1216 Dhaka, Bangladesh.  \nArafat Sahin Afridi [sahin15-3971@diu.edu.bd](sahin15-3971@diu.edu.bd)  \nDepartment ofCSE,  \nMilitary Institute of Science and Technology, Mirpur, 1216, Dhaka, Bangladesh.  \nA.H.M.Saiful Islam [saiful@ndub.edu.bd](saiful@ndub.edu.bd)[ ](saiful@ndub.edu.bd)Department ofCSE,  \nNotre Dame University Bangladesh, Motijheel, 1214, Dhaka, Bangladesh.  \nShaheena Sultana [zareefas.sultana@gmail.com](zareefas.sultana@gmail.com)  \nDepartment ofCSE,  \nNotre Dame University Bangladesh, Motijheel, 1214, Dhaka, Bangladesh.  \nNazmus Nessa Moon [moon@daffodilvarsity.edu.bd](moon@daffodilvarsity.edu.bd)  \nDepartment ofCSE,  \nDaffodil International University, Savar, 1216, Dhaka, Bangladesh.  \nCorresponding Author: Fernaz Narin Nur  \nCopyright © 2025 Shafika Rahman, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nAbstract  \nIn today’s fast-evolving IT landscape, job seekers and recruiters face increasing challenges in aligning candidate qualifications with dynamic job roles and salary expectations. Traditional recruitment systems often rely on manual review or keyword-based algorithms, which lack contextual understanding and do not provide optimal job recommendations. In this paper, we propose JoTPaRS 1 , a Job Title Prediction and Recommendation System that uses ma-  \n1 This is an extended and substantially revised version of the paper “Job Title Prediction and Recommendation System for IT  \nProfessionals” previously published in 5th International Conference on Sustainable Technologies for Industry 5.0 (STI), Dhaka,  \nBangladesh, 2023 .  \n4356  \nCitation: Shafika Rahman, et al. JoTPaRS: A Job Title Prediction and Recommendation System for IT Professionals. Advances in Artificial Intelligence and Machine Learning. 2025;5(3):242 .  \n[https://www.oajaiml.com/ | September 2025](https://www.oajaiml.com/ | September 2025) Shafika Rahman, et al.  \nchine learning and natural language processing to accurately predict relevant IT job titles from user-provided descriptions, skills, and experience levels. In addition to multi-label classification and salary range suggestion, JoTPaRS introduces a novel Game Theory-based optimization layer that selects the most suitable job title among multiple predictions by modeling the decision as a risk-sensitive payoff game. This approach provides users with an optimal ranking of job recommendation, enhancing decision support for both job seekers and recruiters. Using a custom data set of 8,000 IT job descriptions, JoTPaRS achieved a high prediction accuracy of 95 63%, outperforming traditional machine learning models and recent benchmarks based on deep learning. User studies further demonstrate increased satisfaction with the system recommendations after the inclusion of the Game Theory-based refinement. The research suggests that predictive analytics with decision modeling can offera more precise and personalized job matching experience in the digital hiring landscape.  \nKeywords: Game theory, Job titles, Job prediction system, Job descriptions, Job circular links, Machine learning-based system, Salary recommendations.  \n1. INTRODUCTION  \nIn today’s era, there are many different employment types that require different skill sets and certifications due to the Information Technology (IT) industry’s rapid expansion. Although this diversity gives job searchers more options, it also makes the hiring and selection process much m","cbCaiefPWCBtryZ0","https://ap.wps.com/l/cbCaiefPWCBtryZ0","pdf",1381933,23,"English","# Abstract\n## Keywords\n## Introduction","[{\"question\":\"What problem does JoTPaRS address in IT hiring?\",\"answer\":\"JoTPaRS addresses the difficulty of matching candidate qualifications to dynamic IT job roles, where conventional keyword-based or manual screening often fails to capture context and leads to poor shortlisting.\"},{\"question\":\"How does JoTPaRS predict and recommend job titles?\",\"answer\":\"JoTPaRS uses machine learning and natural language processing to predict relevant IT job titles from user-provided descriptions, skills, and experience levels, including multi-label classification for recommendations.\"},{\"question\":\"What is the role of the Game Theory-based optimization layer?\",\"answer\":\"The Game Theory-based layer selects the most suitable job title among multiple predictions by modeling the decision as a risk-sensitive payoff game, improving the ranking of recommendations and user satisfaction.\"}]","JoTPaRS - A Job Title Prediction and Recommendation System for IT Professionals | PDF",1789337042,8]