recommendation system

In recent years, social media platforms have become powerful data collection tools to improve user experience. The vast amount of data generated through social media provides a unique opportunity to develop innovative recommendation systems. This article analyzes the application of data mining methods for social networks in the context of effective recommendation systems, focusing on three key methodologies: sentiment analysis (SA), topic modeling (TM) and social network analysis (SNA), highlighting their positive features.

Today, the improvement of information and technical assistance to users through information technology is relevant. To improve the design of computers, we analyze its components and study the architecture, as well as the process of improving the functionality of a computer. We conducted an analytical review of existing software solutions for analyzing computer components. We consider models for forming a set of recommendations taking into account the wishes of the user. Given the specifics of the analysis of the problem situation, it is proposed to unite users into groups.

The work is devoted to research on the problem of management and organization of free time during the period of forced stay at home by means of information technologies.

Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method.

The development of modern technologies is extremely fast. Every day more and more new and various means appear in the world to improve the quality of life and more. It is not possible for a person to process all this new information on the scale and speed with which this information appears. Everyone has their own preferences and wants to receive and obtain information about certain events or things that they are interested in. This has become one of the most important reasons for creating referral systems.

The recommendation system for content-based movie search has been developed. Used Mongo DB database and utilities with machine learning elements to speed up the search. Using the developed system will save time when selecting movies by certain criteria.

In this paper an analysis of researches and development in information technologies for planning trips and in-travel support for various groups of tourists was provided. It allowed forming the classification of GIS and applications based on these different problems and needs of different groups of tourists. The necessitate for development of geographic information technology tools was proved to provide personalization, optimization and support for tourist in all phases of his travel using mobile technologies of combined with GIS functionality.