[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125521-en":3,"doc-seo-125521-105":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},125521,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","A Comprehensive Review on Machine Learning-Based Job Recommendation Systems","A dynamic, shifting labor market generates overwhelming job postings and makes it difficult for businesses to identify quality candidates while simultaneously challenging job seekers to find suitable roles. Machine learning-driven job recommender systems address these issues by using predictive models to improve job–candidate matching. Hybrid designs that combine collaborative filtering, content-based filtering, and contextual signals (e.g., geography, industry trends, and behavioral data) enhance recommendation accuracy and relevance. This review critically analyzes contemporary techniques, especially hybrid models and algorithmic integrations, and evaluates trade-offs across precision, recall, NDCG, RMSE, and MAE.","International Journal on Robotics, Automation and Sciences  \nA Comprehensive Review on Machine Learning-Based Job  \nRecommendation Systems  \nRui-Ern Yap, Su-Cheng Haw* and Shaymaa Al-Juboori  \nAbstract – A dynamic, constantly shifting labor market creates enormous job postings, overwhelming candidates and making it difficult for businesses to find quality candidates. It is also hard for job seekers to find suitable jobs. Addressing these issues, machine learning-driven job recommender systems have recently become an essential tool using predictive models to improve the match between jobs and candidates. A hybrid design that combines collaborative filtering with content-based filtering and adds contextual information like geographic location, industry trends, and user behavioural data can enhance the accuracy and relevance of recommendations. This paper reviews and critically analyzes contemporary job recommender system techniques. The focus is on hybrid recommendation models and the integration of algorithmic approaches, indicating their strengths and weaknesses. This review also looks into the evaluation metrics like precision, recall, normalized discounted cumulative gain (NDCG), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) . To provide an overall perspective of the various approaches employed and the performance trade-offs inherent therein, this paper hopes to shed some light on the optimization of job recommendation systems for better effectiveness and user satisfaction.  \nKeywords—Recommender System, Machine Learning, Hybrid-based, Job Recommender, Evaluation Metrics, Comprehensive Review.  \nI. INTRODUCTION  \nIn this highly dynamic labor market, with many openings and the candidate profile being diverse, both the job seekers and the employers are confronted with enormous challenges. Traditional matching processes through manual screening or basic keyword searches are usually not effective in finding complex skill sets and, therefore lead to mismatches, inefficient hiring, and suboptimal employment outcomes [1] . Problems of sparsity, scalability, and cold start only to add to the complications of effectively personalizing the recommendation. Although approaches like collaborative filtering and content-based filtering have reduced some of the misery, they are afflicted by synonyms in skill descriptions, adaptive job positions, and the ability to widen existing biases [2] .  \nRecent breakthroughs in Machine Learning (ML), deep learning, and Natural Language Processing (NLP), therefore, offer a range of thrilling possibilities to overcome those limitations. Modern recommender systems are now able to identify advanced patterns or connections between job seekers and job postings due to enormous mining datasets. It is now achievable to use NLP techniques to parse job postings and resumes into more accurate versions, thus creating more enhanced feature extractions and better skillmatching opportunities. The combination of these approaches has led to more robust, more sophisticated systems capable of coping with the  \n􀀍 Corresponding Author email: [sucheng@mmu.edu.my](sucheng@mmu.edu.my) , ORCID: 0000-0002-7190-0837  \nRui-Ern Yap is with Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia (e-mail: [1211105182@student.mmu.edu.my](1211105182@student.mmu.edu.my)) .  \nSu-Cheng Haw is with Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia (e-mail: sucheng@ [mmu.edu.my](mmu.edu.my)) .  \nShaymaa Al-Juboori is with School of Computing, Engineering and Mathematics, University of Plymouth, UK ([shaymaa.al](shaymaa.al)[juboori@plymouth.ac.uk](juboori@plymouth.ac.uk))  \ninherent dynamism and complexity of the contemporary labor market [3] .  \nThis review paper introduces the background of job recommendation systems, examines the shortcomings of traditional approaches, and discusses emerging MLbased methods for effective matchmaking. It emphasizes identifying key feat","cbCaimdUkgF9DUWy","https://ap.wps.com/l/cbCaimdUkgF9DUWy","pdf",655399,1,20,"English","en",105,"# Introduction\n## Challenges of traditional matching\n## Machine learning, deep learning, and NLP for matchmaking\n# Recommender System\n## Overview of recommender systems\n## Interaction matrix and collaborative filtering","[{\"question\":\"Why do traditional job matching methods struggle in a highly dynamic labor market?\",\"answer\":\"Traditional manual screening and basic keyword searches often cannot capture complex skill sets, which can cause mismatches and inefficient hiring. Issues like sparsity, scalability, and cold start further limit effective personalization.\"},{\"question\":\"What makes machine learning-based job recommender systems more effective?\",\"answer\":\"They use predictive models to learn patterns between job seekers and job postings. Techniques such as NLP can extract and align skills from resumes and postings to improve feature matching.\"},{\"question\":\"Which evaluation metrics are reviewed for job recommendation quality?\",\"answer\":\"The review covers precision, recall, NDCG, RMSE, and MAE to assess recommendation effectiveness and model performance trade-offs.\"}]","A Comprehensive Review on Machine Learning-Based Job Recommendation Systems | PDF",1785899592,50,{"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-comprehensive-review-on-machine-learning-based-job-recommendation-systems","",{"@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-comprehensive-review-on-machine-learning-based-job-recommendation-systems/125521/",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},"Why do traditional job matching methods struggle in a highly dynamic labor market?","Question",{"text":75,"@type":76},"Traditional manual screening and basic keyword searches often cannot capture complex skill sets, which can cause mismatches and inefficient hiring. Issues like sparsity, scalability, and cold start further limit effective personalization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What makes machine learning-based job recommender systems more effective?",{"text":80,"@type":76},"They use predictive models to learn patterns between job seekers and job postings. Techniques such as NLP can extract and align skills from resumes and postings to improve feature matching.",{"name":82,"@type":73,"acceptedAnswer":83},"Which evaluation metrics are reviewed for job recommendation quality?",{"text":84,"@type":76},"The review covers precision, recall, NDCG, RMSE, and MAE to assess recommendation effectiveness and model performance trade-offs.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]