User Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business
User Behavior Analysis Using Web-based Machine Learning Features: New Solutions for IT Business

Summary/Abstract: 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 same time, 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 e-business in the future.

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