Algorithmization of processes in recommendation systems

Abstract

The article examines the features of algorithmization of processes in recommendation systems for personnel training and decision support. Such systems are a subclass of information filtering systems that allow building a certain rating based on requests or preferences. The development of recommendation systems is aimed precisely at finding the best solution within a limited period of time. In-depth analysis of complex problems is necessary to develop several alternatives that do differ significantly, including the possibility of inactivity. This is why each alternative should be evaluated. The paper reveals the features of recommendation systems in various fields of application. The conducted research allows us to conclude that there are some significant differences in the nature of providing recommendations to the end user when using distance learning or decision support systems. Recommendations in such systems are shifted from the user's preferences directly to the person and the problem. When structuring the processes of the recommendation system to support decision-making, there is made a focus on the scientific method of substantiating questions. Therefore, the construction of algorithms for such systems is complicated from the first stage – observation, where obstacles or opportunities are identified. Such a problem can be solved through systems of equations that describe processes in an organization with appropriate constraints. Presented in the article algorithm for evaluating and selecting alternatives is one of the options for building a system of recommendations if it is necessary to justify a management decision to expand activities of the company. It is shown that although they lie in the same plane, the solution to remove the problem and the search for an optimal solution are not equivalent. In addition, finding the best solution requires a larger database of information to create a recommendation system.