### State of the Art
Thus, there is a need to extend the GERT approach beyond network characteristics to encompass cognitive and semantic processes within the algorithm for processing user input. Recent research in the field of recommendation systems has increasingly focused on integrating artificial intelligence methods. As a result, intelligent architectures are emerging that can adapt to user preferences, consider semantic relationships, and ensure the interpretability of results. However, despite their promise, these solutions often lack a formal description of the logic of user query processing, its structure, and a probabilistic-temporal model of interaction. The following examples illustrate this. The study [23] presents a method of semantic user profiling based on the extraction of frequent subgraphs in knowledge graphs. User preferences are represented not as vectors but as recurring substructures, allowing the original semantics of interactions to be preserved. These structures are then used in a deep neural network model to generate recommendations. The method demonstrated competitive results and high interpretability. However, despite the high degree of semantic adaptation, there is no step-by-step description of the logic behind the recommendation system's operation or formalization of transitions between phases. This underscores the relevance of models where system logic is described in terms of stochastic processes. The authors of Reference [24] propose a recommendation system architecture based on knowledge graphs. To address the cold-start problem, the system uses clustering and a shared semantic preference map. The system demonstrates adaptability to new users and scalability with large volumes of information. The architecture is described at the level of functional modules, but it lacks a formalized logic for query processing that considers probabilities, categorization errors, and rollbacks. Temporal delays between stages are also not addressed. The GERT approach allows for the formalization of these aspects, supplementing the proposed modular architecture with a quantitative processing model. Reference [25] describes a comprehensive intelligent approach that includes reinforcement learning, graph representations, and knowledge distillation. The system is aimed at explainability and robustness of recommendations in the field of career navigation. Reinforcement learning allows for consideration of the user's trajectory, while knowledge distillation enables adaptation to more computationally efficient models. However, although the model exhibits high cognitive complexity, it remains within the domain of neural network learning and does not formalize the internal stages of algorithm logic. The structure of interaction between query processing phases, transition probabilities, and temporal characteristics are not presented in an analytical form, which limits interpretation and mathematical modeling. Reference [26] implements the following two-phase architecture: the first phase cleans data using a sentiment analysis model, and the second phase trains the recommendation model solely on \"clean\" data. This approach addresses the impact of low-quality input on final recommendations. The authors demonstrate that this scheme increases the accuracy and robustness of the system. At the same time, despite the inclusion of input validation, the architecture lacks a formal description of the logic of transitions and processing stages. Possible rollbacks, refinement loops, or the probability of categorization errors are not modeled. The work is valuable as a data improvement tool but requires supplementation with a stochastically described logic of phase transitions, as implemented in the GERT approach. Foundational works by [PERSON] and [PERSON] [27] laid the groundwork for classifying recommendation techniques, while [PERSON] [28] introduced the taxonomy of hybrid systems. [PERSON], [PERSON] and [PERSON] extensively covered practical implementations and domain-specific adaptations [29]. Additionally, [PERSON] et al.