| Abstract [eng] |
The rapid development of digital technologies and artificial intelligence (AI) has significantly transformed the service sector, particularly in the fields of personalization, user experience management, and data-driven decision-making. AI-based recommendation systems are widely applied in commercial digital platforms to reduce information overload, support user decision-making, and increase engagement. However, in the cultural services sector, the application of AI personalization solutions remains fragmented and insufficiently explored, despite increasing challenges related to audience attention fragmentation, information overload, and declining regular participation in live cultural activities. The object of the thesis is the application of artificial intelligence personalization solutions in cultural services recommendation platforms. The aim of the thesis is to develop an empirically grounded theoretical model for the application of AI personalization solutions in a cultural services recommendation platform. The tasks of the thesis are: 1. To analyze the concept of artificial intelligence, its main components, and application possibilities in the service sector; 2. To systematize the theoretical foundations of personalization and AI-based personalization models and assess their impact on user behavior and decision-making; 3. To examine the application of AI personalization solutions in the cultural services sector at both national and international levels; 4. To develop a theoretical model for applying AI personalization solutions in a cultural services recommendation platform; 5. To refine the proposed theoretical model based on empirical research results. Work methods: The theoretical part of the thesis is based on a comparative analysis of scientific literature, document analysis and abstraction methods, focusing on artificial intelligence, personalization processes, recommendation systems, and cultural services management. The empirical part employs a mixed exploratory research design. First, secondary data analysis was conducted using Eurostat and Lithuanian Council for Culture data to identify patterns of youth cultural consumption and trends in digital behavior. Second, a quantitative survey was carried out, and the collected data were analyzed using descriptive and correlation statistical methods. Finally, a qualitative focus group study was conducted with young participants to deepen the interpretation of the quantitative results and refine the theoretical model based on user experiences, expectations, and assessments of information accessibility. Results: The study revealed that youth cultural consumption is increasingly fragmented, situational, and strongly shaped by digital environments dominated by mobile technologies and algorithmically structured information flows. While young people actively consume cultural content in a broad sense, their regular participation in live cultural events remains limited and highly dependent on their life stage, time constraints, and access to information. Empirical findings confirmed that AI-based personalization solutions have the potential to reduce information overload, support decision-making, and increase perceived relevance of cultural offers. The refined theoretical model integrates behavioral data analysis, recommendation generation, and feedback mechanisms, demonstrating how AI personalization can support data-driven cultural management and create functional, emotional, social, and identity-related value for young audiences. The results provide practical insights for the future development of cultural services recommendation platforms that combine technological innovation with cultural sector specificity. |
