collaborative filtering

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 Technology Intelligent Search of Content in E-commerce Systems

The article describes the process of developing intelligent search technology for content for the implementation of the module of e-commerce systems for forming a list of recommendations for regular users. Intelligent search of content is based on methods of linguistic analysis, modern algorithms for parsing and finding words, and recommendations based on user preferences.

Models and methods for building web recommendation systems

Modern Word Wide Web contains a large number of Web sites and pages in each Web site. Web recommendation system (recommendation system for web pages) are typically implemented on web servers and use the data obtained from the collection viewed web templates (implicit data) or user registration data (explicit data). In article considering methods and algorithms of web recommendation system based on the technology of data mining (web mining).

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.