ALGORITHMIC SUPPORT OF THE WEB SERVICE RECOMMENDATION SYSTEM FOR LEARNING FOREIGN LANGUAGES

Abstract

This work is devoted to the analysis of algorithmic support of multimedia content recommender systems and the development of a web service toincrease the efficiency of learning foreign languages using a recommender system that personalized the selection of educational content for the user.To form a list of necessary multimedia content, the main criteria of the recommender system were selected, the basic needs of users were identified,which the system should solve, since increasing the efficiency of learning a foreign language is achieved not only by choosing teaching methods, butalso by watching multimedia content, namely news, films, educational videos, clips, etc. Therefore, in order to form a list of the necessary multimediacontent, the main criteria of the recommender system were formed, the main needs of users were identified, which the system must solve. From theside of the method for implementing algorithmic support, various types of data filtering were considered, from modern technical methods to librariesto ensure the functionality of the system, and the algorithm based on hybrid filtering was chosen, in which known user ratings are used to predict thepreferences of another user. Functional requirements have been developed and a web service has been proposed that allows a comprehensive impact onuser learning when learning a foreign language, software implementation of which is made using Java Script, Python and additional libraries. Thisimplementation allows you to build a process for tracking changes in user requirements and transfer information to the database (DB) and, afteranalyzing the input data, change the proposed multimedia content to the user. In the course of further research, it is planned to conduct practicalexperiments, taking into account the specifics of certain methods of teaching foreign languages and the use of statistical data to assess the effectivenessof the algorithm of the proposed recommender system.