http://bijournal.hse.ru en-us Copyright 2025 Tue, 01 Apr 2025 12:11:37 +0300 https://bijournal.hse.ru/en/2025--1 Vol.19/1030722641.html       Recommendation systems are widely used in the commercial field. The algorithms and architectures of recommendation systems are similar in various fields of application and have proven their effectiveness. Recommendations are based on the user’s profile, the manner of his behavior on various IT (Information Technology) resources, as well as on similar users. At the same time, the use of recommendation systems in specialized areas is not widespread. Technology divisions are a promising new area of application for recommendation systems, and IT experts themselves will be the users. The purpose of this article is to consider a combination of a recommendation system, machine learning (ML) and LLM (Large Language Model) and to design these tools in a single system. Data volumes are currently measured in petabytes (1015 bytes) and exabytes (1018 bytes). In order to process even technical information (metadata/technodata) from the surrounding IT landscape, from the IT systems used by experts, AI (Artificial Intelligence) agents are needed. This article provides a literature review regarding the use of recommendation systems in combination with LLM applications, and suggests an application architecture model that generates human-readable news from technical event logs. The system is designed for a group of users who work with big data (ML engineers, data analysts, and data researchers). It is a combination of recommendation system technologies, LLM, and machine learning models. The article also provides the first results of the research that was carried out. https://bijournal.hse.ru/en/2025--1 Vol.19/1030723823.html       This article is devoted to the problem of determining the sequence of project implementation in a program of improving business processes of an organization. The relevance of the study is related to current conditions, where the quality of business processes is essential not just for the success, but also for the survival of an organization. Improvement of business processes is a costly program that involves certain projects. The projects of the program cannot be started at the same time due to limited budget and human resources. Thus, we face the task of determining the sequence of stages of program implementations. The solution of this task is one of the most important problems of business informatics. This paper proposes a new criterion for prioritizing projects. It takes into account the fact that funds for projects are generated during the implementation of business processes. The criterion also takes into account the pace of spending the project budget and the need for participation of key employees of the organization. The implementation of the program is divided into a few stages. At each stage, the problem is solved by determining a set of projects whose sum of priorities is maximum and whose resource requirements do not exceed the constraints developed at that stage. The relevance of the article is initiated by looking at the need of enterprises that ensure the airworthiness of civil aviation airplanes. This work is of interest for project program managers of production and service companies, as well as for a wide range of researchers. https://bijournal.hse.ru/en/2025--1 Vol.19/1030734554.html       Modern cities are facing increasing traffic congestion, necessitating the implementation of intelligent traffic management systems. One of the key areas in this field is adaptive traffic signal control, which can adjust to changing traffic conditions. However, existing methods for optimizing traffic signal cycle parameters have several limitations, such as high computational complexity, the risk of premature convergence of algorithms and the difficulty of accounting for traffic dynamics. This study proposes an approach to optimizing the characteristics of an intelligent transportation system using hybrid evolutionary algorithms. The methods we developed combine the principles of genetic algorithms (GA) and particle swarm optimization (PSO), enabling a balance between global and local search for optimal parameters. The research examines six different hybridization schemes, including modified versions of basic algorithms, as well as their integration with HDBSCAN clustering methods for adaptive optimization frequency tuning. To evaluate the effectiveness of the proposed algorithms, a simulation model was developed in the AnyLogic environment, replicating real urban traffic conditions. Numerical experiments conducted on a local section of the road network in Moscow demonstrated that the hybrid SlipToBest algorithm achieves the best results in reducing average travel time and fuel consumption, while the Alternating algorithm ensures high solution stability. The results of this study confirm the feasibility of using hybrid evolutionary methods for traffic flow management tasks. The proposed algorithms not only enhance the efficiency of traffic signal control but also establish a foundation for the further development of adaptive urban traffic management systems. https://bijournal.hse.ru/en/2025--1 Vol.19/1031008394.html       This study was conducted within the framework of the urgent task of studying the processes of developing the human capital of an organization and increasing employee productivity. At the same time, the development process is viewed through the prism of creating and implementing various elements of the well-being program into the main corporate business processes of the organization. The purpose of this work is to develop a fuzzy method for forming an optimal portfolio of well-being program activities which will allow you to get as close as possible to the target values of key performance indicators (KPIs) of employees on a given planning horizon. To achieve this goal, a hypothesis is put forward about the possibility of building a tool that allows, based on the functional dependencies of influence channels, to form an optimal portfolio of well-being program activities that increases the efficiency of the organization. The method developed consists of a model representing a fuzzy programming problem and a method for finding its solution. A distinctive feature of the model is the consideration of two levels of uncertainty in the formation of an optimal portfolio of activities related to the reliability of estimates of numerical coefficients of functional dependencies of channels of influence and a set of parameters of constraints determined by experts. An integral indicator is used as the target function of the model, which characterizes the degree to which the target values of key employee performance indicators are achieved, taking into account the importance of each of them for the organization. The optimization variables in the model are binary variables that determine the inclusion of a certain event in the well-being program of an organization at a specific time within a given planning period. The limitations in the model are: the total amount of financial resources allocated for the implementation of the well-being program; the amount of investment in a specific area of the well-being program; an increase in the integral indicator of competence of each employee. From a practical point of view, the proposed method will make it possible to form a well-founded portfolio of well-being program activities, the implementation of which has the maximum possible positive impact on employee productivity. https://bijournal.hse.ru/en/2025--1 Vol.19/1031009798.html       The processes of formation and development of intellectual capital in the digital economy are ill structured processes occurring in conditions of a significant increase in the speed and unpredictability of changes in the external environment. This makes it extremely difficult to use previous experience and probabilistic forecasts when assessing the risks of failure to achieve strategic goals for the development of the intellectual capital of an organization. At the same time, undesirable deviations in achieving these goals can lead to significant negative consequences. In this regard, there is a need to develop appropriate fuzzy methods and models, all of which determines the relevance of this work. The purpose of this study was to develop a fuzzy method for assessing the risks of failure to achieve the strategic goals of an organization in the field of intellectual capital development. The method is based on a fuzzy model developed by the authors which allows us to take into account the uncertainty tolerance of the decision maker. Testing the method on the example of a specific organization showed the possibility of its practical applicability. We provide quantitative assessments and qualitative interpretations of the risk levels of failure to achieve target indicators for the development of the intellectual capital of an organization (a large regional university). https://bijournal.hse.ru/en/2025--1 Vol.19/1031020877.html      In mass real estate valuation, in cadastral valuation, there is a problem of splitting the value of a single real estate object into the value of land plot and buildings (improvements) located on it. One of the key information sources for real estate valuation is market data. Such data may contain information on offer prices, as well as actual transaction prices (for example, in mortgage transactions) for the whole object. At the same time, in the accounting policy of enterprises different rates of land and property tax often require separate accounting of the value of land plots and the buildings located on them. The problem of such splitting of a single object’s value is the subject of permanent discussions in the valuation community. There are no established methods. This article proposes a method of splitting the value of a single property object based on the approach borrowed from co-operative game theory. A simple game formulation of the problem and its fair solution based on the Shepley value are considered. Simple and well-interpretable computational formulas are obtained, which allow us to split the market value of single objects on large data sets in minimum time. The proposed method is new in the theory and practice of valuation.