[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118417-en":3,"doc-seo-118417-105":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},118417,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Intelligent Product Recommendation System for E-Commerce using Machine Learning","E-commerce platforms increasingly rely on machine learning to deliver personalized product recommendations that adapt to evolving user behavior. Compared with static approaches, learning-based models can analyze interactions, purchase histories, and browsing patterns to generate more accurate, context-sensitive suggestions. This project builds collaborative filtering, content-based filtering, and hybrid recommendation models, using Logistic Regression as a core supervised learning method. The system targets higher customer engagement, improved sales conversions, and greater user satisfaction through real-time preference understanding.","International Journal of Innovative Research in Science  \nEngineering and Technology (IJIRSET)  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.699 Volume 14, Issue 4 , April 2025  \n|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|  \nVolume 14, Issue 4, April 2025  \n|DOI: 10.15680/IJIRSET.2025.1404487|  \nIntelligent Product Recommendation System for E-Commerce using Machine Learning  \nS.Babu[1], K S Bharath[2], G Sridhar [3], Kota Lakshmi [4], C Adesh [5]  \nAssistant Professor, Department ofCSE, Kuppam Engineering College, JNTUA University, Kuppam,  \nAndhra Pradesh, India 1  \nUG Students, Department ofCSE., Kuppam Engineering College, JNTUA University, Kuppam,  \nAndhra Pradesh, India2-5  \nABSTRACT: E-commerce platforms increasingly leverage machine learning to enhance user experience through personalized product recommendations. While traditional static systems often fail to adapt to changing user behavior, machine learning models can dynamically analyze user interactions, purchase history, and browsing patterns to deliver tailored suggestions. This project implements collaborative filtering, content-based filtering, and hybrid recommendation models to generate accurate and relevant product recommendations. The system is designed to improve customer engagement, boost sales, and enhance overall user satisfaction. The ultimate goal is to develop an intelligent recommendation engine capable of understanding individual user preferences and providing real-time, context-aware product suggestions.  \nKEYWORDS: E-Commerce, Product Recommendation, Machine Learning, Logistic Regression, Collaborative Filtering, Content-Based Filtering, Hybrid Recommendation System, User Behavior Analysis, Personalized Recommendations.  \nI. INTRODUCTION  \nThe e-commerce industry has witnessed exponential growth in recent years, offering customers a vast selection of products. With this abundance of choices, consumers often face difficulty in identifying products that match their preferences. Recommendation systems have become essential tools for e-commerce platforms, helping users navigate this complexity by suggesting items that align with their interests and past behaviors.  \nTraditional recommendation systems typically rely on either collaborative filtering or content-based filtering. While effective to an extent, these methods often encounter challenges such as data sparsity, cold start problems, and limited adaptability to dynamic user behavior. To address these issues, machine learning techniques have emerged as a powerful alternative, offering the ability to analyze patterns in user data and make data-driven predictions.  \nThis project proposes an intelligent product recommendation system that integrates collaborative filtering, contentbased filtering, and a hybrid approach, enhanced using machine learning techniques. In particular, Logistic Regression is used as the core algorithm to learn from user interaction patterns and product features. The system analyzes user purchase histories, ratings, and browsing behavior to recommend products that are most likely to be of interest to each user.  \nBy leveraging a combination of traditional filtering methods and supervised learning, the system aims to improve the accuracy and relevance of recommendations. The primary objective is to create a dynamic, real-time recommendation engine that enhances user satisfaction, increases sales conversions, and delivers a more engaging shopping experience.  \nIJIRSET©2025 | An ISO 9001:2008 Certified Journal | 9413  \n|[www.ijirset.com](www.ijirset.com |A Monthly)[ |A Monthly](www.ijirset.com |A Monthly), Peer Reviewed & Refereed Journal| e-ISSN: 2319-8753| p-ISSN: 2347-6710|  \nVolume 14, Issue 4, April 2025  \n|DOI: 10.15680/IJIRSET.2025.1404487|  \nFig 1.1 Product Recommendation System  \nII. LITERATURE REVIEW  \nRe","cbCaimrtoGIR9XXe","https://ap.wps.com/l/cbCaimrtoGIR9XXe","pdf",1280174,1,9,"English","en",105,"# I. Introduction\n# II. Literature Review","[{\"question\":\"What problem does the proposed recommendation system address?\",\"answer\":\"It tackles the difficulty users face in finding products matching their preferences and overcomes limitations of traditional static recommendations by adapting to changing behavior.\"},{\"question\":\"Which recommendation techniques are integrated in this project?\",\"answer\":\"The system integrates collaborative filtering, content-based filtering, and hybrid recommendation models, enhanced with machine learning.\"},{\"question\":\"Why is Logistic Regression used in the system?\",\"answer\":\"Logistic Regression is used as the core algorithm to learn from user interaction patterns and product features to improve prediction accuracy for tailored recommendations.\"}]","Intelligent Product Recommendation System for E-Commerce using Machine Learning | PDF",1785683519,23,{"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},"intelligent-product-recommendation-system-for-e-commerce-using-machine-learning","",{"@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/intelligent-product-recommendation-system-for-e-commerce-using-machine-learning/118417/",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},"What problem does the proposed recommendation system address?","Question",{"text":75,"@type":76},"It tackles the difficulty users face in finding products matching their preferences and overcomes limitations of traditional static recommendations by adapting to changing behavior.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which recommendation techniques are integrated in this project?",{"text":80,"@type":76},"The system integrates collaborative filtering, content-based filtering, and hybrid recommendation models, enhanced with machine learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is Logistic Regression used in the system?",{"text":84,"@type":76},"Logistic Regression is used as the core algorithm to learn from user interaction patterns and product features to improve prediction accuracy for tailored recommendations.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]