### State of the Art  
The development of recommender systems has progressed through several distinct stages over the past three decades. In the 1990s, early implementations were primarily based on collaborative filtering techniques, which laid the foundation for personalized content delivery by analyzing patterns in user behavior [3]. The availability of standardized datasets such as MovieLens significantly contributed to benchmarking and popularizing these approaches. During the 2000s, hybrid recommendation models emerged, combining collaborative filtering with content-based methods to overcome challenges such as data sparsity and cold-start problems. At the same time, probabilistic graphical models and matrix factorization techniques began to be incorporated, increasing the scalability and robustness of recommender algorithms [4]. In the 2010s and beyond, a new generation of intelligent recommender systems has emerged, integrating advanced semantic technologies, knowledge graphs, deep learning, and reinforcement learning [5]. These systems emphasize personalization, explainability, context-awareness, and adaptability, aiming to model not only static preferences but also user intent and session dynamics. Recent studies also highlight the need for transparent and interpretable systems capable of modeling uncertainty, temporal aspects of interaction, and system reliability [6]. In this regard, the application of probabilistic time-based methods becomes increasingly relevant. However, despite significant algorithmic advancements, most modern architectures focus on improving accuracy or scalability, often overlooking the formal modeling of structural and temporal logic of recommendation workflows. This gap is particularly evident in domain-specific systems, such as personalized search in cultural heritage or museum collections, where uncertainty, iteration, and human feedback loops play a central role. Given the practical relevance of the development, the literature references are grouped as follows (Table 1). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference(s)** & **Focus Area** & **Key Contributions** & **Identified Limitations** \\ \hline
[7; 8; 9; 10; 11] & General recommendation systems; museum and cultural applications & Survey of algorithm types; early personalization methods; domain-specific applications (e.g., museum tours, semantic collections) & Lack of formal workflow modeling; no probabilistic analysis; no timing structure \\ \hline
[12; 13; 14; 15] & Data structuring and user validation & Approaches to input filtering, sentiment analysis, and semantic profiling & Input validation treated as preprocessing; no modeling of error recovery or iterative feedback \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative summary of related work on recommendation systems. The study [7] is a review of the current state of research in the field of recommendation systems, including their typology, methodological foundations, existing challenges, and practical application areas. It emphasizes the role of recommendation algorithms as tools to overcome information overload amid the exponential growth of digital content. The authors analyze various classes of recommendation systems, focusing on their applicability in areas such as e-commerce, healthcare, transport, agriculture, and digital media. Despite its depth and broad scope, the article remains at a high conceptual level. It does not include a description of architectural models focused on structuring user queries, input data validation, step-by-step information processing, or analysis of iterative returns. It also does not address categorization error handling or the logic of forming user models in conditions of unstructured or ambiguous input. The review [8] covers the current state of recommendation systems, including method classification, analysis of robustness, biases, and algorithm fairness. The article discusses personalized and group systems and explores quality metrics and issues related to interpretability.