[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121608-en":3,"doc-seo-121608-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},121608,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Performance Evaluation of Machine Learning Techniques on Resolution Time Prediction in Helpdesk Support System - Paper","Accurate estimation of incident resolution time is essential for effective customer service resource allocation. This paper benchmarks widely used machine learning techniques for resolution time prediction in a helpdesk support system. The pipeline includes data preprocessing to remove outliers and missing values and assess resolution-time distribution irregularities, followed by automatic feature selection using statistical techniques. Different learning models are trained and evaluated by prediction accuracy and model fitting time to identify the best-performing approach and clarify factors influencing resolution durations.","International Journal on Robotics, Automation and Sciences  \nPerformance Evaluation of Machine Learning Techniques on Resolution Time Prediction in Helpdesk Support System  \nTong-Ern Tai, Su-Cheng Haw*, Wan-Er Kong and Kok-Why Ng  \nAbstract – Estimating incident resolution times accurately is critical to maintaining an effective resource allocation for customer service. In order to meet this need, this paper explores machine learning techniques widely applied in the Resolution Time Prediction and identify the performance of chosen approaches via benchmarking dataset. The proposed method starts with data preprocessing, such as removing outliers and missing values and determining any irregularities in the resolution times distribution. Subsequently, we automatically choose the most relevant features using various statistical techniques. As the last stage of our prediction pipeline, we will apply different machine learning approaches the dataset to find the effectiveness of model and conclude the best technique based on the model accuracy and model fitting time. By applying this strategy, we hope to gain abetter understanding of the factors affecting incident resolution times, which will eventually result in better resource allocation and planning for customer support operations.  \nKeywords—Resolution Time Prediction, Machine Learning, Ticketing System, Customer Service, Recommender System.  \nI. INTRODUCTION  \nBusinesses always highly value comprehensive customer service, and customers have the greatest expectations regarding swift resolution. Whenever customers file a service ticket for assistance, they expect a prompt, clear, and practical answer. In order to provide customers with a great experience and maintain the company's reputation, customer service representatives work hard to address issues assigned to them while handling a large number of requests every day. Ayodeji et al. assert that prompt service delivery can boost client happiness and loyalty [1], which in turn can promote repeat and future business [2,3] .  \nThe resolution time prediction is the projection of the duration required for a customer support agent to address a customer's problem, question, or grievance. Apart from that, responding to customer inquiries as quickly as feasible would also greatly enhance customer loyalty. The goal of automating this process by estimating the time needed to handle specific issues based on cases similar to previous ones has been made possible by developing cutting-edge technologies such as Artificial Intelligence (AI) and machine learning (ML) . ML advancements allow for the  \n*Corresponding Author email: [sucheng@mmu.edy.my](sucheng@mmu.edy.my) ORCID: 0000-0002-7190-0837  \nTong-Ern Tai is with Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia (e-mail: tai.tong.ern@ [student.mmu.edu.my](student.mmu.edu.my)) .  \nSu-Cheng Haw is with Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia (e-mail: [sucheng@mmu.edu.my](sucheng@mmu.edu.my)) .  \nWan-Er Kong is with Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia (e-mail: [kong.wan.er@](kong.wan.er@)[ ](kong.wan.er@)[student.mmu.edu.my](student.mmu.edu.my)) .  \nKok-Why Ng is with Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia (e-mail: [kwng@mmu.edu.my](kwng@mmu.edu.my)) .  \nInternational Journal on Robotics, Automation and Sciences (2024) 6, 2:59-68  \nautomation of ticket classification, which in turn enables the prediction of case resolution times [4,5] .  \nSeveral services, including banking, meal preparation, tickets, and gadget maintenance, function on a take-turn basis and demand a lengthy wait from their clients. These sectors have accumulated extensive diversified data and case studies throughout time, allowing the computer model to forecast the amount of time to resolve an issue. If customers are unaware of how long it will take to resolve their issue","cbCaicqb0LNfEwvH","https://ap.wps.com/l/cbCaicqb0LNfEwvH","pdf",599053,1,10,"English","en",105,"# Introduction\n## Resolution time prediction in customer service\n## Machine learning for ticket classification and RTP\n## Research questions\n# Material and Method\n## Background on ML techniques","[{\"question\":\"Why is resolution time prediction important in helpdesk support?\",\"answer\":\"It supports swift customer issue handling and improves customer experience. It also helps allocate customer service resources more effectively by estimating how long similar cases take to resolve.\"},{\"question\":\"What preprocessing and feature selection steps does the proposed method include?\",\"answer\":\"The approach removes outliers and missing values, checks irregularities in the resolution time distribution, and automatically selects relevant features using statistical techniques.\"},{\"question\":\"How is model performance evaluated to choose the best machine learning technique?\",\"answer\":\"Models are compared using prediction accuracy and model fitting time, alongside common evaluation metrics such as RMSE, MSE, and MAE.\"}]","Performance Evaluation of Machine Learning Techniques on Resolution Time Prediction in Helpdesk Support System - Paper | PDF",1785736462,25,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"performance-evaluation-of-machine-learning-techniques-on-resolution-time-prediction-in-helpdesk-support-system-paper","",{"@graph":36,"@context":86},[37,54,69],{"@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/performance-evaluation-of-machine-learning-techniques-on-resolution-time-prediction-in-helpdesk-support-system-paper/121608/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is resolution time prediction important in helpdesk support?","Question",{"text":76,"@type":77},"It supports swift customer issue handling and improves customer experience. It also helps allocate customer service resources more effectively by estimating how long similar cases take to resolve.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What preprocessing and feature selection steps does the proposed method include?",{"text":81,"@type":77},"The approach removes outliers and missing values, checks irregularities in the resolution time distribution, and automatically selects relevant features using statistical techniques.",{"name":83,"@type":74,"acceptedAnswer":84},"How is model performance evaluated to choose the best machine learning technique?",{"text":85,"@type":77},"Models are compared using prediction accuracy and model fitting time, alongside common evaluation metrics such as RMSE, MSE, and MAE.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]