[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127158-en":3,"doc-seo-127158-105":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},127158,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A Preference Compositional Approach for Client Structured Web Customer Segmentation Using Machine Learning Techniques","The web information system faces exponential growth, making data classification and customer segmentation increasingly tedious and difficult to manage effectively. Traditional customer segmentation methods often fall short in scale, modification, and verification, while redundancy, incorrect inputs, and irrelevant records degrade segmentation quality. This research proposes a machine-learning based preference compositional process for client structured web customer segmentation, aligning customer online request/response behavior with segmentation needs. The approach supports more reliable segmentation and enables future AI-driven integration in web information systems.","A Preference Compositional Approach for Client Structured Web Customer Segmentation Using Machine Learning Techniques  \nAdlin Selva Golda. V1 , Narayani. V2,  \n1Research Scholar, Manonmaniam Sundaranar University, Tirunelveli,  \nEmail: [adlingolda@gmail.com](adlingolda@gmail.com)  \n2Assistant Professor, Department of/Computer Science, St Xavier’s College, Tirunelveli  \nAbstract: The web information system develops in an exponential growth in which the data classification and segmentation are tedious process to handle in an effective way. The process of handling vast amount of web information with the target of segmented grouping entirely depends on the nature of the data along with the approach of segmentation. The existing customer segmentation methods lacks in the areas of scale, modification and verification. The main issues of redundancy, incorrect and irrelevant data plays its substantial role in degrading the performance of segmentation methodology. This research article proposes a machine learning approach for handling client structured web customer segmentation with the preference compositional process based on their requirements of online web requests and responses. In near future this research article leads the path for the incorporation of artificial intelligence based customer segmentation in web information system.  \nKeywords: Machine learning, web data, segmentation, information system, customer data  \nI. INTRODUCTION  \nSegmentation:  \nSegmentation means to divide the marketplace into parts, or segments, which are definable, accessible, actionable, and profitable and have a growth potential. In other words, a company would find it impossible to target the entire market, because of time, cost, and effort restrictions [1] . It needs to have a 'definable' segment - a mass of people who can be identified and targeted with reasonable effort, cost and time [2] .  \nCustomer Segmentation:  \nCustomer segmentation is the process of dividing a customer base into distinct groups of individuals that have similar characteristics [3] . This process makes it easier to target specific groups of customers with tailored products, services, and marketing strategies [4] . By segmenting customers into different classes, businesses can better understand their needs, preferences, and buying patterns, allowing them to create more personalized and effective marketing campaigns.  \nMachine Learning:  \nMachine Learning is the field of study that gives computers the capability to learn without being explicitly programmed [5] . ML is one of the most exciting technologies that one  \nwould have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn [6] .  \nWeb information System:  \nWeb information system, or web-based information system, is an information system that uses Internet web technologies to deliver information and services, to users or other information systems/applications.  \nII. METHODOLOGY  \nThe proposed methodology comprises 3 levels of implementation. They are  \na. Corrective motion-Issues removal  \n1. Removal of redundancy  \nThe hash method approach is used to remove the redundant data in the structured web customer segmentation process.  \nCustomer information is converted with hash function for matrix representation. The process of comparing the customer data using hash values are time efficient and produces more effective outputs. The deleted redundant data improves the performance in customer segmentation.  \n2. Removal of incorrect data  \nThe removal of incorrect data includes the process of identifying the source and recognizes the type of error.  \nThe authentication of source data can be done through proper validation on sensitive customer financial information system like PAN, Aadhar etc.  \n3. Remove irrelevant data  \nThe removal of irrelevant data focuses on structural errors removal and removing the unwanted components in the customer information ","cbCaisjiRjI69OcZ","https://ap.wps.com/l/cbCaisjiRjI69OcZ","pdf",1341613,1,10,"English","en",105,"# Introduction\n## Segmentation\n## Customer Segmentation\n## Machine Learning\n## Web information System\n# Methodology\n## Corrective motion - Issues removal\n## Improvisation - Dealing with existing methods slackness\n## Preference Compositional approach - Optimal approach selection","[{\"question\":\"Why do existing customer segmentation methods perform poorly at scale?\",\"answer\":\"Existing methods often lack adequate scale, modification, and verification, and they suffer when redundant, incorrect, or irrelevant data is present, which reduces segmentation performance.\"},{\"question\":\"What are the three levels in the proposed methodology?\",\"answer\":\"The methodology consists of three implementation levels: Corrective motion-Issues removal, Improvisation for dealing with slackness in existing methods, and a Preference Compositional approach for optimal selection.\"},{\"question\":\"How does the preference compositional approach structure customer segmentation?\",\"answer\":\"It uses four stages: Customer x Product mapping composition, customer segmentation categorization composition, computation of customer segmentation means, and construction of the customer segmentation preference approach using clustering methods.\"}]","A Preference Compositional Approach for Client Structured Web Customer Segmentation Using Machine Learning Techniques | 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do existing customer segmentation methods perform poorly at scale?","Question",{"text":75,"@type":76},"Existing methods often lack adequate scale, modification, and verification, and they suffer when redundant, incorrect, or irrelevant data is present, which reduces segmentation performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the three levels in the proposed methodology?",{"text":80,"@type":76},"The methodology consists of three implementation levels: Corrective motion-Issues removal, Improvisation for dealing with slackness in existing methods, and a Preference Compositional approach for optimal selection.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the preference compositional approach structure customer segmentation?",{"text":84,"@type":76},"It uses four stages: Customer x Product mapping composition, customer segmentation categorization composition, computation of customer segmentation means, and construction of the customer segmentation 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