Identification of User Behavior Patterns in Machine Perception Systems Using Machine Learning

The information about the user in the systems is used everywhere in the modern world – from the sphere of advertising to security systems. This kind of data is especially popular in the field of personalized information systems and machine perception systems, which include recommendation systems. In turn, machine learning is a popular tool for solving problems of machine perception, including the prediction of effective recommendations. However, despite the fact, that machine learning includes the variety of methods, the key question of any model is its quality: how and what can be improved in the model in order to improve it? Is it possible to improve the quality of the model by including new data about the user and how to take into account the important features of his or her behavior? The purpose of this work is to investigate the possibility of identifying user behavior patterns and suggest an approach to their consideration in machine perception systems using machine learning methods with the example of recommender systems. Therefore, the study is based on the hypothesis that the use of data on user behavior patterns improves the quality of recommendations in the recommendation systems that use machine learning methods to generate recommendations.