Recommender system

Large-scale recommender systems using Hadoop and collaborative filtering: a comparative study

With the rapid advancements in internet technologies over the past two decades, the amount of information available online has exponentially increased.  This data explosion has led to the development of recommender systems, designed to understand individual preferences and provide personalized recommendations for desirable new content.  These systems act as helpful guides, assisting users in discovering relevant and appealing information tailored to their specific tastes and interests.  This study's primary objective is to assess and contrast the latest methods utilized

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

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.

Some methods in software development recommendation systems

This article analyzes the current state of the models and methods of building recommendation systems. The basic classes of problems that solve the recommendation system are highlighted. The features of the method collaborative filtering are shown. Developed a method for calculating the similarity coefficients, taking into account the sparseness of ratings vectors of goods and people.

Fuzzy Model for Recommender Systems

The paper analyzes the current state of development and application of recommendation systems, models and methods of construction of recommendation systems. It is shown that the most widely used method came into collaborative filtering. The method of fuzzy clustering is developed, which improves the accuracy of predicting ratings of products.