The aim of this thesis is to investigate how personalized recommendations based on artificial intelligence affect consumer behavior in e-commerce. Special attention is paid to understanding how these recommendations can improve the customer experience, increase customer satisfaction and encourage repeat purchases. The purpose of the research is to provide insight into the effectiveness of recommendation systems and to identify key factors that contribute to their success. In the theoretical part of the work, previous studies and literature dealing with personalization in e-commerce, artificial intelligence and consumer behavior are analyzed. Through this literature review, a deeper understanding of the fundamental principles and challenges accompanying the application of artificial intelligence technology in e-commerce was gained. The empirical part of the work is based on a questionnaire that was distributed among e-commerce users. Random sampling was used to ensure the representativeness of the results, and the collected data were analyzed using statistical methods to determine correlations between variables. The results of the survey reveal different attitudes of respondents towards personalized recommendations in online shopping. The majority of respondents show moderate trust in such recommendations, while a smaller number of respondents express a high degree of trust or some skepticism. Respondents mostly make decisions based on their own needs and preferences. The conclusion of the thesis emphasizes that personalized recommendations, generated by artificial intelligence, significantly influence consumer behavior in e-commerce, increasing customer satisfaction and the tendency to make impulsive purchases. There is a need for further development of algorithms to better match individual customer preferences. Also, the importance of an ethical approach, including transparency and a balance between personalization and consumer autonomy, was emphasized in order to ensure success in e-commerce.