MACHINE LEARNING ALGORITHMS FOR DEVELOPING RECOMMENDATION SYSTEMS

The article discusses algorithms and technologies for constructing a recommendation system associated with a machine learning system for solving various artificial intelligence problems. An overview of modern machine learning algorithms for building recommendation systems is presented. Today, in the customer relationship management market, there is a need for automated procedures for predicting future customers using recommendation mechanisms. Functions for searching for “similarity,” i.e., potential clients similar to existing clients, and for viewing lists of clients divided into categories such as location or line of business are already widely available. Modern recommendation systems are typically built using machine learning algorithms. Thus, it is interesting to determine which machine learning algorithms are best suited for building a recommendation engine aimed at predicting customers. To build a user interface, recommendation systems based on machine learning algorithms use modern web and database technologies to combat information overload, such as Django and PostgreSQL.

recommendation systems, machine learning algorithms, collaborative filtering, neural networks, classification algorithms, clustering

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2026-03-17