### State of the Art
A comparative experiment is conducted with eight algorithms, ranging from simple popularity-based models to graph neural networks, demonstrating that proper segmentation can significantly improve the accuracy of certain models. However, the article only considers superficial aggregation of user attributes without incorporating structural or step-by-step query processing. There are no mechanisms for error control in categorization, rollbacks to previous steps, or analysis of the sequential dynamics of refining user input. Additionally, there is no description of the logic behind the formation of user models in terms of probabilities or the time required to complete various stages. The article [13] proposes an advanced scheme for semantic user profiling based on the analysis of frequent subgraphs in knowledge graphs. The goal of the work is to preserve the semantic interpretability of the user model when using neural network algorithms. It addresses the problem of meaning loss that arises when using feature vectors in deep learning. The authors successfully integrate substantive links between the user and the subject domain, preserving the semantic context. However, the architecture remains static: there is no description of a structure for step-by-step processing, validation, or reformulation. There is no analysis of rollbacks, error probabilities, or a mechanism for collecting accurate feedback in cases of user preference misunderstandings. The study [14] presents a two-phase architecture of a recommendation system, in which the key component is a module for verifying the quality of input data. In the first phase, a sentiment predictor is used to cleanse user reviews of inconsistent \"rating-comment\" pairs, after which the cleaned data are passed on to the core recommendation model. The article raises an important issue regarding the quality of user input and its impact on the final recommendation. However, the system logic lacks an explicit structural decomposition--data validation is implemented as preprocessing, without inclusion in an iterative query processing cycle. There is also no description of how ambiguities or incorrect categories are handled, nor are rollback transitions or error probabilities modeled. The article [15] proposes an architectural framework for a personalized recommendation system focused on mobile applications. To address the cold start problem and real-time personalization, the system uses machine learning methods, content-based and collaborative filtering, as well as user segmentation. The system includes preference adaptation based on feedback, which can be interpreted as a dynamic form of data validation. However, there is no formalized step-by-step model for query processing, nor a mechanism for evaluating categorization errors. The study [16] presents the results of a meta-analysis of the state of recommendation systems. The authors classify research into five areas, including architectural and computational aspects, modeling of user behavior, and algorithmic data processing methodologies. Attention is given to critical issues such as interest drift, multitasking, goal conflicts, data leaks, and training set poisoning, all of which directly affect model robustness and accuracy. It is noted that the architectures of most systems suffer from insufficient adaptability and weak support for complex user interaction scenarios. Despite its broad scope, the work remains at a review level: it does not offer a formal step-by-step description of the internal logic of query processing algorithms, lacks decomposition into stages (categorization, validation, rollback, generation), and does not consider probabilistic temporal characteristics. This highlights the need for structural GERT models, which describe algorithms through equivalent graphs with probabilities and delays. The review [17] focuses on the evolution of recommendation methods in the era of big data.