Information system of feedback monitoring in social networks for the formation of recommendations for the purchase of goods

Abstract

This paper describes an information system for monitoring and analyzing reviews on social networks to form recommendations for the purchase of goods. This system is designed to be used by customers to speed up and facilitate the search for the necessary products on e-commerce resources. Successful selection of a quality product according to the desired criteria is extremely important, as it saves search time and customer money. Analyzing comments on the network, the information system recommends the product if there is a preponderance of positive feedback on it. The purpose of the work, object and subject of research, scientific novelty and practical significance of the work are formulated. An analysis of the peculiarities of the studied subject area and known means of solving the problem was carried out. Systems used in online marketing were used as a prototype system for generating recommendations based on feedback analysis. Comparative characteristics of the system with analogues were conducted and it was determined that the system is unique, and its development is relevant, since known existing similar systems do not recommend products to users based on the feedback of other users. The general goal of system development is determined, the purpose, place of application of the system, development and implementation of the system are described. The criteria that are put forward when defining the goals are defined. Using the method of analysis of hierarchies, it was determined that the type of product being developed is a decision support system. A conceptual model of the system has been developed. Project requirements are modeled – business requirements, user requirements, functional requirements, non-functional requirements. Input and output data of the system are defined. The decision-making system is based on an algorithm for sentiment analysis of social networks users using the logistic regression method. Logistic regression is one of the most common machine learning algorithms that is easy to implement for classifying sets of linearly separable clusters of data. It quickly learns on large data sets and guarantees reliable results. Economic, functional, financial and time effects should be expected from the implementation of such a recommendation system.