OVERVIEW OF MODELS AND ALGORITHMS FOR OPTIMIZING APPLICATION INTERFACES BASED ON USER BEHAVIORAL DATA

Abstract

The article examines mathematical models and algorithms for optimizing application interfaces based on user behavior analysis. The main goal of the research is to improve the quality of user interaction with applications by enhancing interface design and functionality. Special attention is given to studying user behavioral data. User behavior data (clicks, interaction time, sessions, errors, frequency of returning to the app) were collected and analyzed to identify patterns that affect the usability and efficiency of interfaces. The paper provides a detailed review of modern models used for evaluating and improving application interfaces, which analyze user behavior data and take into account their actual needs. Criteria for interface quality and ways to achieve them were identified. The study establishes goals and methods for optimizing design and interaction to ensure they are effective and convenient for users. The article also outlines the prospects for implementing optimization models, highlighting key challenges associated with deploying models and algorithms for automating interface improvements, as well as suggesting directions for further research in this area. The proposed models consider key behavioral data such as click count, interaction time, session numbers, and errors, allowing the identification of behavior patterns that inform decisions to enhance user experience. A/B testing and regression analysis algorithms are used to test the effectiveness of the changes. The article also provides recommendations for developers and designers on improving usability, performance, and user engagement through personalization and enhanced interface accessibility. The implemented models and algorithms enable efficient adaptation of interfaces to real user needs, contributing to improved overall application performance and a better user experience.