Using recommender systems on the basis of machine learning methods in the framework of e-trade teaching

Abstract

The aim of this study is to research the processes of formation of recommender information systems in e-trade and to analyze the process of teaching reference systems for use within the framework of e-trading training course. The objectives of the study are to research of general problematics of the use of recommender e-trading systems, analysis of existing approaches to the construction of recommender information systems in electronic trade, studying the mechanisms of implementation of recommender information systems in the mechanisms of electronic trading. The object of the study is the processes of identifying consumer priorities in e-trading. The subject of the study is the recommender systems and using information management systems for electronic trade in the university's educational process. The work analyzes, summarizes and systemizes research on the problem of the use of recommender information systems in e-trading has been carried out. The types and models of the recommender information systems are analyzed, methods and algorithms of building recommender systems on the basis of machine learning are determined, the possibilities of using machine learning algorithms for construction of different types of reference systems are considered, tools for developing the recommender system in electronic trade are explored. Tasks are developed for a laboratory practicum of electronic trading education course. The results of the study are planned to summarize for development of recommendations for the using of e-trading recommender systems in the learning process.