The rapid technological development of recent years, the improvement of computer systems, and the familiarization of a large percentage of the world's population with the digital world have given an enormous boost to e-commerce, which is continually evolving and serving more needs. Simultaneously, the significant increase of users and products, coming as a result of this progress, and the dynamic entry of machine learning and data science in the field of information technology has allowed e-commerce sites to improve the browsing experience significantly. Nowadays, e-commerce sites provide users with personalized product suggestions that meet their preferences, which means a simultaneous increase in sales for online stores. In addition to personalized direct product recommendations to consumers, there are also advertising views (or banners). They are quite common on e-commerce websites, aiming to help and promote consumer product groups to the consumer according to his preferences or by categorizing him according to key elements of his electronic imprint. Personalized banner recommendations have not been studied to the same degree as product personalization and are more applicable to large e-commerce platforms. This dissertation aims to design and build a real-time personalized banner recommendation system for a medium-sized online e-shop with real-time data based on machine learning methods and algorithms. In the context of the work, we propose a novel framework that takes into account the actions of the users during their navigation, known as "clickstream" data. The proposed framework effectively recognizes user interests and suggests banners that correspond to their preferences.