[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123855-en":3,"doc-seo-123855-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123855,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","User Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business","The article examines how the growth of IT business increases demand for running machine learning models directly in the client browser, lowering server load and reducing access layers. It compares client-side feature trade-offs, including reduced transmitted information and limits of device power. Tensorflow.js is positioned as a tool for classifying and clustering users, predicting behavioral trends, detecting anomalous or suspicious actions, and building recommendation models. The proposed model is trained on Instagram and Facebook news-post data using activity parameters (likes, comments, shares), supporting technical and economic development and informing future work on innovation management systems in e-business.","[www.ssoar. info](www.ssoar. info)  \nUser Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business  \nMysi uk , Roman; Kononenko , Ole ks ii; Svystovych , Andriy; Ozhy hov, Ole ks ii; Osadets , Nazar; Kuch mak , Yuriy; Poh reb nia k , Andr ii; Honsor, Yuriy  \nVeröffentlichungsversion / Published Version Zeitschriftenartikel / journal article  \nEmpfohlene Zitierung / Suggested Citation:  \nMysiuk , R. , Kononenko , O. , Svystovych , A. , Ozhyhov, O. , Osadets , N. , Kuchmak , Y. , ... Honsor, Y. (2024) . User Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business. Path of Science , 10(5), 1001-1007. [https://doi.org/10.22178/pos.104-5](https://doi.org/10.22178/pos.104-5)  \nNutzungsbedingungen:  \nDieser Text wird unter einer CC BY Lizenz (Namensnennung) zur Verfügung gestellt. Nähere Auskünfte zu den CC-Lizenzen finden Sie hier:  \n[https://creativecommons.org/licenses/by/4.0/deed.de](https://creativecommons.org/licenses/by/4.0/deed.de)  \nTerms of use:  \nThis document is made available under a CC BY Licence (Attribution). For more Information see:  \n[https://creativecommons.org/licenses/by/4.0](https://creativecommons.org/licenses/by/4.0)  \nUser Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business  \nRoman Mysiuk 1, Oleksii Kononenko 2, Andriy Svystovych 2, Oleksii Ozhyhov 2, Nazar Osadets 2, Yuriy Kuchmak 2, Andrii Pohrebniak 2, Yuriy Honsor 2  \n1 Ivan Franko National University of Lviv  \n1 Unіversytetska Street, Lvіv, Ukraine, 79000  \n2 Lviv University of Business and Law  \n99 Kulparkіvska Street, Lviv, 79021, Ukraine  \nDOІ: 10.22178/pos.104-5  \nJEL Classification: D11, D83, M29, M49  \nReceіved 27.04.2024 Accepted 25.05.2024 Publіshed onlіne 31.05.2024  \nCorresponding Author: Roman Mysiuk  \n[mysyuk@ukr.net](mysyuk@ukr.net)  \n© 2024 The Authors. Thіsartіcle іs lіcensed under a Creatіve Commons Attrіbutіon 4.0 Lіcense   \nAbstract. The development of information technologies in IT business increases the interest in executing machine learning models directly on the client browser, reducing the load on the server and the number of levels of access to it. At the sametime, some features have advantages and disadvantages, associated with a smaller amount of information transmitted over the network, limited power of client devices, and others. Among modern client-side tools with machine learning capabilities, Tensorflow.js is suitable, which can be used to analyse user behaviour in web applications for classification and clustering models based on their behavioural patterns, predict future user behaviour trends, detect unusual or suspicious user actions, recommendation models based on their previous behaviour. The article analyses the features of implementation and the limitations associated with the use, specifically regarding the behaviour of users in social networks. The model was formed based on data from news posts on social networks Instagram and Facebook, with the following parameters of user activity, such as the number of likes, comments, and shares according to the post's text. These aspects are a significant addition to the tools that can be applied within the economic, technical, and other means of IT business development. Considering this, it is advisable to study the formation and development of the innovation management system in ebusiness in the future.  \nKeywords: business; IT business; machine learning; tensorflow; user behaviour analysis; data analysis; social network; data processing; е-business development.  \nINTRODUCTION  \nIn today's world, analysing user behaviour helps to understand the needs and preferences of users, develop a personal algorithm for selecting content in social networks, improve developed products and offer new services. In addition, the analysis helps to ensure Internet security and prevent fraud. Thus, a new term, User Behavior Analytics (UBA), appeared worldwide.  \nUser Behavior Analytics (","cbCaivhfs7Prk0MZ","https://ap.wps.com/l/cbCaivhfs7Prk0MZ","pdf",698464,1,"English","en",105,"# Introduction\n## User Behavior Analytics (UBA)\n## Client-side machine learning with Tensorflow.js\n# Data and Model Construction\n## Social network data sources (Instagram, Facebook)\n## User activity parameters\n# Implementation Features and Limitations\n## Classification, clustering, prediction, and anomaly detection\n## Recommendation based on prior behavior\n# Implications for IT Business Development","[{\"question\":\"What is User Behavior Analytics (UBA) in the context of this article?\",\"answer\":\"UBA is described as tracking, analyzing, and interpreting user actions and interactions in a digital environment, such as websites or applications, to extract preferences, patterns, and trends.\"},{\"question\":\"Why are client-side machine learning models important for IT business applications?\",\"answer\":\"Running models in the browser can reduce server load and network transfer by transmitting less data, while also supporting privacy by keeping data on the user’s device.\"},{\"question\":\"How is the proposed model built for user behavior analysis?\",\"answer\":\"The model is formed using data from news posts on Instagram and Facebook, using user activity parameters including likes, comments, and shares based on the post text.\"}]","User Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business | 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