This master's thesis explores the concepts of algorithmic content recommendation on digital platforms, focusing on user perception. Analyzing concepts such as platformization, datafication, and surveillance capitalism, the research delves into a deeper understanding of the impact of algorithmic marketing on interactions between users and digital platforms. Platformization, as a key feature of the contemporary digital environment, transforms how users communicate, spend, and engage with products and services. Datafication, leveraging the wealth of available consumer data, enables personalized marketing messages through algorithms. The concept of surveillance capitalism investigates how consumer data is utilized for profit generation and control over their preferences. The research methodology is based on qualitative semi-structured interviews with users. This method facilitates a profound understanding of their attitudes, perceptions, and experiences related to algorithmic marketing on digital platforms. Through interview analysis, the research has identified users' subjective experiences with digital platforms, their reactions to personalized advertising strategies, and the impact of these strategies on their experience and satisfaction. By investigating these concepts and employing the qualitative interview method, this study contributes to understanding the dynamics between algorithmic data processing and users of digital platforms. The results of this research can provide valuable insights into user subjective understanding of how algorithms influence their interactions with information, other people, and the external world.