[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116896-en":3,"doc-seo-116896-105":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},116896,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Predicting Customer Purchase Intention Using Online Machine Learning Methods","E-commerce has expanded rapidly, yet many users browse without purchase intent, interacting with products and then abandoning the site, reducing revenue. This browsing behavior generates large-scale data that can be used to convert visitors into buyers and to refine marketing strategies while improving customer experience. Real-time intent understanding supports data-driven decision-making, and machine learning enables models to infer user objectives from behavioral signals. Prior research has achieved accurate purchase-intention predictions but often relies on offline learning requiring retraining. This thesis evaluates online machine learning approaches that update continuously, using probabilistic, linear, and tree-based methods on a UCI ML dataset and examines the effect of feature dimensionality. Model performance is assessed using AUC, sensitivity, and specificity, and results indicate online classifiers are promising, with tree-based models showing overall better performance.","Predicting Customer Purchase Intention Using Online Machine Learning Methods  \nEleftheria Trigeni  \nSID: 3308210043  \nSCHOOL OF SCIENCE & TECHNOLOGY  \nA thesis submitted for the degree of Master of Science (MSc) in Data Science  \nAPRIL 2023  \nTHESSALONIKI – GREECE  \nPredicting Customer Purchase Intention Using Online Machine Learning Methods  \nEleftheria Trigeni  \nSID: 3308210043  \nSupervisor: Dr. D. Karapiperis  \nSupervising Committee Mem- Assoc. Prof. K. Diamantarasbers: Assist. Dr. P. Koukaras  \nSCHOOL OF SCIENCE & TECHNOLOGY  \nA thesis submitted for the degree of Master of Science (MSc) in Data Science  \nAPRIL 2023  \nTHESSALONIKI – GREECE  \nAbstract  \nE-commerce has gained significant popularity and this trend is expected to grow even more. The users do not always visit an e-commerce with purchase intention but with browsing one meaning that they interact with the products but ultimately abandon the website without offer revenue to the business. This is a challenge that any e-commerce faces. Users interacting with it generate a huge volume of data that business can benefit from to convert browsers into buyers and simultaneously to adjust the marketing strategies accordingly with aim to improve customer experience. Understanding customer intent in real-time is considered vital to improve data-driven decisions. Machine learning offers the framework to build models that can identify the objective of a user. Many studies have utilized it and managed to predict purchase intention with high accuracy. None of them took the advantage of online machine learning that allows the models tobe updated continuously without retraining needed. This study utilized probabilistic, linear and tree-based online machine learning methods with goal to achieve this, using a well-known experimental dataset from UCI ML repository. In addition, the impact of features dimensionality on the models ’ performance examined. The models evaluated in terms ofAUC, sensitivity and specificity and the results suggested that online classifiers are considered promising to achieve this task, with tree-based model pointing out an overall better performance.  \nEleftheria Trigeni 2023  \nContents  \nABSTRACT ................................................................................................................. III  \nCONTENTS ...................................................................................................................V  \nLIST OF TABLES ......................................................................................................VII  \nLIST OF FIGURES ....................................................................................................VII  \n1 INTRODUCTION......................................................................................................1  \n1.1 PROBLEM DEFINITION ........................................................................................4  \n1.2 RESEARCH QUESTIONS......................................................................................4  \n1.3 DISSERTATION OUTLINE ....................................................................................5  \n2 BACKROUND ..........................................................................................................7  \n2.1 MACHINE LEARNING AND ITS IMPACT IN E-COMMERCE.....................................7  \n2.2 OFFLINE AND ONLINE MACHINE LEARNING .......................................................8  \n2.2.1 Offline Machine Learning ................................................................ 9  \n2.2.2 Online Machine Learning ................................................................ 9  \n2.2.3 Data Streams .................................................................................... 11  \n2.1 CLASSIFICATION MODELS ................................................................................ 12  \n2.1.1 Hoeffding tree .................................................................................. 12  \n2.1","cbCaiqSZwF2uvVJP","https://ap.wps.com/l/cbCaiqSZwF2uvVJP","pdf",2143854,1,84,"English","en",105,"# Abstract\n# Contents\n# List of Tables\n# List of Figures\n# 1 Introduction\n## 1.1 Problem Definition\n## 1.2 Research Questions\n## 1.3 Dissertation Outline\n# 2 Background\n## 2.1 Machine Learning and Its Impact in E-Commerce\n## 2.2 Offline and Online Machine Learning\n## 2.2.1 Offline Machine Learning\n## 2.2.2 Online Machine Learning\n## 2.2.3 Data Streams\n## 2.1 Classification Models\n## 2.1.1 Hoeffding tree\n## 2.1.2 Hoeffding Adaptive Tree\n## 2.1.3 Extremely Fast Decision Tree Classifier\n## 2.1.4 Naive Bayes\n## 2.1.5 Passive Aggressive\n## 2.1.6 SGD Classifier\n## 2.2 Evaluation Metrics\n## 2.2.1 Evaluation of online classifiers\n# 3 Related Work\n## 3.1 Purchase Intention Prediction\n## 3.2 Online Machine Learning Techniques\n# 4 Experimental\n## 4.1 Data Source\n## 4.2 Descriptive Statistics\n## 4.3 Data Preprocessing\n## 4.4 Feature Importance\n# 5 Methodology\n## 5.1 Tools Used\n## 5.2 Deal With Imbalance\n## 5.3 Standardization","[{\"question\":\"Why is predicting customer purchase intention important for e-commerce?\",\"answer\":\"Many users browse without buying and abandon the site, hurting revenue. Predicting purchase intention helps businesses convert browsers into buyers and adjust marketing strategies while improving customer experience.\"},{\"question\":\"What advantage does online machine learning provide over offline approaches in this thesis?\",\"answer\":\"Online machine learning updates models continuously without requiring retraining. This supports more responsive intent prediction as new user data arrives.\"},{\"question\":\"Which online learning model families are evaluated, and how is performance measured?\",\"answer\":\"The study evaluates probabilistic, linear, and tree-based online machine learning methods. Performance is measured using AUC, sensitivity, and specificity, and results show tree-based online models perform best overall.\"}]","Predicting Customer Purchase Intention Using Online Machine Learning Methods | PDF",1785672320,212,{"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},"predicting-customer-purchase-intention-using-online-machine-learning-methods","",{"@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/predicting-customer-purchase-intention-using-online-machine-learning-methods/116896/",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","2026-08-02",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 is predicting customer purchase intention important for e-commerce?","Question",{"text":75,"@type":76},"Many users browse without buying and abandon the site, hurting revenue. Predicting purchase intention helps businesses convert browsers into buyers and adjust marketing strategies while improving customer experience.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What advantage does online machine learning provide over offline approaches in this thesis?",{"text":80,"@type":76},"Online machine learning updates models continuously without requiring retraining. This supports more responsive intent prediction as new user data arrives.",{"name":82,"@type":73,"acceptedAnswer":83},"Which online learning model families are evaluated, and how is performance measured?",{"text":84,"@type":76},"The study evaluates probabilistic, linear, and tree-based online machine learning methods. 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