Development of the architecture of a recommendation system for choosing online courses

The article provides a rationale for the relevance of developing a recommender system in the field of e-learning. The main approaches to building a recommender system are analyzed: collaborative, content and hybrid filtering. The main objects of the recommender system for choosing online courses are presented: the student, training modules (online courses), elements of knowledge that the user can receive at the end of the training. In algorithmic support, methods for creating recommender systems, such as machine learning, neural networks, genetic algorithms, are considered. Problems in the methods of building recommender systems have been identified: sparseness; cold start; scalability; searching for elements that are most likely to be preferred by the user from a common set of elements. The main problem of recommender systems is to obtain an accurate and high-quality recommendation for the selection of educational objects in accordance with user preferences. It is concluded that it is necessary to build an architecture of a recommender system, including a model of an individual learning trajectory. Filtration of educational objects occurs with the help of a genetic algorithm. The expediency of using a microservice approach to create a web application is determined. The functional tasks of the developed system are highlighted, such as data collection, analysis of user requests, the formation of educational objects using an individual learning trajectory and the issuance of recommendations for choosing online courses. An algorithm for the functioning of the recommender system, a scheme for the operation of the recommender system, as well as information support for the operation of this system have been developed. A general approach to the development of a universal recommender system that can be integrated into the client's service is proposed. The purpose of developing a recommender system for choosing online courses is to provide students with the most appropriate learning objects (sequence of objects) to study in accordance with the characteristics of the student and fragments of knowledge (competencies).