In this article adaptive clustering algorithm for recommender systems is developed.
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.