Abstract
Nowadays, there are more and more web applications of all kinds. Each of them solves a specific problem and makes life easier for its users. Web applications come in many different types: from a platform for learning courses and watching movies to an online store selling goods. The best systems are those that make things as easy as possible for the user, behave like old friends who know the behavior and tastes of their users and can predict their next move. It would be useful to integrate such system behavior into an online store system, as nowadays, a huge number of people prefer to buy goods online, saving time and effort. Thus, recommender systems have become an important tool for improving the efficiency of e-commerce stores and ensuring customer satisfaction. This study analyzes the main approaches to the application of recommender systems for online stores, substantiates the advantages and feasibility of the selected technologies for the implementation of an online store information system using neural networks.
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