[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122306-en":3,"doc-seo-122306-105":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},122306,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Efficient Machine Learning Algorithms in Hybrid Filtering-Based Recommendation Systems - Research Paper Survey","Widespread use of e-commerce platforms has intensified demand for automatic recommendation systems driven by machine learning. Although many ML-based recommenders and analyzers exist, most rely on a single filtering strategy and limited prediction approaches using clustering. This paper provides a systematic year-wise survey of hybrid filtering categories, compared across analysis methods, learning factors, datasets, performance, and limitations. It highlights strong contributions in collaborative filtering and deep learning, and proposes an efficient Deep Learning hybrid filtering recommendation system (HFRS-DL) with multi-layer stages for improved recommendation quality.","Efficient Machine Learning Algorithms in Hybrid Filtering-Based  \nRecommendation Systems  \nRuchika *  \n*Corresponding author, Assistant Professor, Amity Institute of Information Technology, Amity  \nUniversity Uttar Pradesh, [India. E-mail: bathla.ruchika@gmail.com](India. E-mail: bathla.ruchika@gmail.com)  \nMayank Sharma   \nAssistant Professor, Amity Institute of Information Technology, Amity University Uttar Pradesh, [India. E-mail: msharma22@amity.edu](India. E-mail: msharma22@amity.edu)  \nSyed Akhter Hossain   \nPh.D., Dean, School of Science and Engineering, Canadian University of Bangladesh, Dhaka, 1212, Bangladesh. E-mail: [akhter.hossain@cub.edu.bd](akhter.hossain@cub.edu.bd)  \n\n| Abstract\u003Cbr>The widespread use of E-commerce websites has drastically increased the need for automatic recommendation systems with machine learning. In recent years, many ML-based recommenders and analyzers have been built; however, their scope is limited to using a single filtering technique and processing with clustering-based predictions. This paper aims to provide a systematic year-wise survey and evolution of these existing recommenders and analyzers in specific deep learning-based hybrid filtering categories using movie datasets. They are compared to others based on their problem analysis, learning factors, data sets, performance, and limitations. Most contributions are found with collaborative filtering using user or item similarity and deep learning for the IMDB datasets. In this direction, this paper introduces a new and efficient Hybrid Filtering-based Recommendation System using Deep Learning (HFRS-DL), which includes multiple layers and stages to provide a better solution for generating recommendations.\u003Cbr>Keywords: Recommender System; Content-Based Filtering; Collaborative Filtering; Movie Recommendation; Deep Learning. |  |\n| --- | --- |\n| Journal of Information Technology Management, 2023, Vol. 15, Issue 3, pp. 134-161 | Received: April 03, 2023 |\n| Published by the University of Tehran, Faculty of Management | Received in revised form: June 13, 2023 |\n| doi: [https://doi.org/ 10.22059/jitm.2023.93631](https://doi.org/ 10.22059/jitm.2023.93631) | Accepted: July 20, 2023 |\n| Article Type: Research Paper\u003Cbr>© Authors | Published online: August 26, 2023\u003Cbr> |\n\nIntroduction  \nE-commerce and marketing companies use data and improve sales through promotional systems on their websites and programs. The application of Recommender Systems (RS) has been increasing steadily in recent years. They have been instrumental in E-commerce, improving customer experience, product promotion, and product ratings. It eliminates the tyranny of choices, smoothing the way for decision-making and increasing online sales. Nowadays, the use and applications of RS are taking their pace with various Machine Learning (ML) techniques.  \nMachine learning is a branch of computer science where we learn about computer algorithms that automatically improve with the help of experience and data usage. Artificial intelligence (AI) includes machine learning as a subset. It uses training data and modelling to generate predictions and judgments. The applications of ML have taken a giant form and have widespread use cases in various fields like sentiment analysis, generating recommendations, email filtering, and image processing, etc. Deep learning is a subfield of ML from various data abstractions and representation levels. Many industries, corporations, and companies already use Deep Learning-based RS (DLRS) built upon different neural networks to improve customer experience. For Example, YouTube, Netflix, eBay, Twitter, etc., choose deep neural networks, while apps like Spotify use a Convolutional Neural Network (CNN) . Deep learning-based recommender systems cope with complex interaction patterns and precisely reflect users' preferences. Given effective feature extraction, CNN is a good fit for unstructured multimedia data processing. Also, CNN helps us remove the cold ","cbCaiaN7tX9vsLj8","https://ap.wps.com/l/cbCaiaN7tX9vsLj8","pdf",2042307,1,28,"English","en",105,"# Introduction\n## Motivation and role of recommender systems in e-commerce\n## Machine learning, AI, and deep learning in recommender systems\n## DL-based recommender system benefits and examples\n# Paper structure (overview of subsequent sections)","[{\"question\":\"Why are hybrid filtering-based recommendation systems important for e-commerce?\",\"answer\":\"They support personalized recommendations that improve customer experience, product promotion, ratings, and online sales by reducing choice overload and enabling better decision-making.\"},{\"question\":\"What limitations of existing recommender approaches does the paper address?\",\"answer\":\"Many ML-based recommenders and analyzers are limited to a single filtering technique and rely on clustering-based prediction, restricting coverage of hybrid deep-learning scenarios.\"},{\"question\":\"What does the proposed HFRS-DL system include?\",\"answer\":\"The paper introduces a new efficient Hybrid Filtering-based Recommendation System using Deep Learning (HFRS-DL), featuring multiple layers and stages to generate improved recommendations.\"}]","Efficient Machine Learning Algorithms in Hybrid Filtering-Based Recommendation Systems - Research Paper Survey | PDF",1785809914,71,{"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},"efficient-machine-learning-algorithms-in-hybrid-filtering-based-recommendation-systems-research-paper-survey","",{"@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/efficient-machine-learning-algorithms-in-hybrid-filtering-based-recommendation-systems-research-paper-survey/122306/",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","2026-08-04",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 are hybrid filtering-based recommendation systems important for e-commerce?","Question",{"text":75,"@type":76},"They support personalized recommendations that improve customer experience, product promotion, ratings, and online sales by reducing choice overload and enabling better decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations of existing recommender approaches does the paper address?",{"text":80,"@type":76},"Many ML-based recommenders and analyzers are limited to a single filtering technique and rely on clustering-based prediction, restricting coverage of hybrid deep-learning scenarios.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the proposed HFRS-DL system include?",{"text":84,"@type":76},"The paper introduces a new efficient Hybrid Filtering-based Recommendation System using Deep Learning (HFRS-DL), featuring multiple layers and stages to generate improved 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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]