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

Abstract

The development of information technologies in IT business increases the interest in the execution of machine learning models directly on the client browser, reduces the load on the server and the number of levels of access to it. At the same time, there are some features that have advantages and disadvantages, which are 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 analyze user behavior in web applications for classification and clustering models based on their behavioral patterns, predict future user behavior trends, detect unusual or suspicious user actions, recommendation models based on their previous behavior. The article analyzes the features of implementation, the limitations associated with the use specifically for the behavior of users in social networks. The model was formed on the basis of data from news posts on social networks Instagram and Facebook with the following parameters of user activity as the number of likes, comments and shares according to the text of the post. These aspects are a significant addition to the tools that can be applied within the set of economic, technical and other means for IT business development. Taking this into account, in the future it is advisable to study the formation and development of the innovation management system in e-business.