Development of a Database Management System of Recommendation Systems for Computer Networks and Computer-integrated Systems

Abstract

The goal of this work is to develop a database management system of the recommendation system for computer networks and computer-integrated systems, as well as to compare the quality of its work with existing systems. Today, recommendation systems are widely used in computer networks, in particular, in social networks, Internet commerce systems, media content distribution, advertising, etc., as well as in computer-integrated systems, in particular, in the Internet of Things and smart houses. An effective way to present the data required for the recommendation system can reduce the number of resources required and facilitate the development and use of more sophisticated algorithms for compiling lists of recommendations. When storing data from the recommendation system, one of the important parameters of the database is the speed of reading/writing information, as well as the amount of memory required to store data in one format or another. Therefore, it is advisable to use simple data models. This paper investigated the feasibility and effectiveness of using open linear lists to store recommendation system data in computer networks and computer-integrated systems. To test the effectiveness of the proposed method of presenting data in the recommendation system, comparative experiments were conducted with such software as: relational database management system Postgresql, resident repository key-value pairs Redis and graph database Neo4j. Each method of presenting data was tested on the following indicators: time of filling the repository with test data; the amount of memory occupied by the repository after filling; recommendation generation time. The MovieLens data set was used as test data. The developed database management system based on linear lists is significantly ahead of the existing tools in terms of both speed and efficiency of memory use.